Self-supervised learning and pseudo-labelling
Summary
TLDRThis video delves into self-supervised learning and pseudo-labeling, key concepts in machine perception. It highlights the limitations of supervised learning and explores how self-supervised learning, inspired by human multimodal learning, can improve model performance. The video discusses various pretext tasks and the instance discrimination approach, which leverages visual similarity for representation learning. Additionally, it covers pseudo-labeling in semi-supervised learning, demonstrating its effectiveness in tasks like word sense disambiguation and image classification, showcasing its potential in handling large unannotated datasets.
Takeaways
- 🤖 **Self-Supervised Learning Motivation**: The need for self-supervised learning arises from the limitations of supervised learning, where large annotated datasets are required, and models still make mistakes that humans wouldn't.
- 👶 **Inspiration from Human Development**: Human babies learn in an incremental, multi-modal, and exploratory manner, which inspires self-supervised learning methods that mimic these natural learning strategies.
- 🔄 **Redundancy in Sensory Signals**: Self-supervised learning leverages redundancy in sensory signals, where the redundant part of the signal that can be predicted from the rest serves as a label for training a predictive model.
- 🧠 **Helmholtz's Insight**: The concept of self-supervision is rooted in Helmholtz's idea that our interactions with the world can be thought of as experiments to test our understanding of the invariant relations of phenomena.
- 🐮 **Self-Supervised Learning Defined**: It involves creating supervision from the learner's own experience, such as predicting outcomes based on movements or changes in the environment.
- 🔀 **Barlow's Coding**: Barlow's work suggests that learning pairwise associations can be simplified by representing events in a way that makes them statistically independent, reducing the storage needed for prior event probabilities.
- 📈 **Pseudo-Labeling**: Pseudo-labeling is a semi-supervised learning technique where a model predicts labels for unlabeled data, which are then used to retrain the model, often leading to improved performance.
- 🎲 **Pretext Tasks**: In computer vision, pretext tasks like image patch prediction or jigsaw puzzles are used to train models without explicit labeling, hoping they learn useful representations of the visual world.
- 🔄 **Instance Discrimination**: A powerful self-supervised learning approach that trains models to discriminate between individual instances, leading to better visual similarity capture compared to semantic class discrimination.
- 🔧 **Practical Challenges**: There are practical challenges in creating effective pretext tasks, such as the risk of models learning to 'cheat' by exploiting unintended cues rather than understanding the task's underlying concepts.
Q & A
What are the two main topics discussed in the video?
-The two main topics discussed in the video are self-supervised learning and pseudo-labeling.
What is the motivation behind self-supervised learning?
-The motivation behind self-supervised learning is to improve machine perception by taking inspiration from early stages of human development, particularly the way humans learn in a multi-modal and incremental manner.
How does self-supervised learning differ from supervised learning?
-In self-supervised learning, the learner creates its own supervision by exploiting redundant signals from the environment, whereas in supervised learning, a model is trained using manually annotated data.
What is the role of redundancy in sensory signals in self-supervised learning?
-Redundancy in sensory signals provides labels for training a predictive model by allowing the learner to predict one part of the signal from another, thus creating a learning target without external supervision.
What is a pretext task in the context of self-supervised learning?
-A pretext task is a task that is not the final goal but is used to learn useful representations of the data. It is often a game-like challenge that the model must solve, which in turn helps it learn about the visual world.
What is pseudo-labeling and how does it work?
-Pseudo-labeling is a semi-supervised learning algorithm where a classifier is first trained on labeled data, then used to predict labels for unlabeled data. These predicted labels, or pseudo-labels, are then used to retrain the classifier, often iteratively.
Why is pseudo-labeling effective when large quantities of data are available?
-Pseudo-labeling is effective with large data sets because it leverages the unlabeled data to improve the classifier's predictions, leading to better performance, especially when combined with techniques like data augmentation.
What is the 'noisy student' approach mentioned in the video?
-The 'noisy student' approach is a method where an initial model trained on labeled data infers pseudo-labels for unlabeled data, and then a higher capacity model is trained on these pseudo-labeled data, often with heavy use of data augmentation.
How does self-supervised learning relate to human perception development?
-Self-supervised learning relates to human perception development by mimicking the way humans learn from their environment through exploration and interaction, without the need for explicit labeling.
What challenges are there in creating effective pretext tasks for self-supervised learning?
-Creating effective pretext tasks can be challenging because they need to be designed carefully to ensure the model learns useful representations rather than exploiting unintended shortcuts or low-level signals.
Outlines
🤖 Introduction to Self-Supervised Learning and Pseudo-Labeling
This paragraph introduces the video's focus on self-supervised learning and pseudo-labeling, topics derived from lectures given at the University of Cambridge. The speaker outlines the structure of the video, starting with self-supervised learning and then moving to pseudo-labeling. The motivation for self-supervised learning is discussed in the context of machine perception, highlighting the limitations of supervised learning despite its successes. The speaker points out that even high-capacity models make mistakes and suggests looking to human developmental stages for inspiration. The learning strategies of human babies are examined, emphasizing incremental, social, physical, exploratory, language-based, and multi-modal learning. The challenges of implementing these strategies in machine learning are acknowledged, especially the practical barriers to embodied learning. The paragraph concludes by setting the stage for a discussion on multi-modal learning and self-supervised methods inspired by human learning.
🔍 The Concept of Self-Supervised Learning
The paragraph delves into the concept of self-supervised learning, which involves creating one's own supervision. It references historical insights by Helmholtz and Barlow on perception and redundancy in sensory signals. The importance of redundancy for learning new associations is emphasized, as it provides a predictable signal from which to learn. The discussion then turns to computational tricks, such as using minimum entropy coding to avoid a combinatorial explosion of storage for prior event probabilities. The paragraph also covers the evolution of self-supervised learning, from early works by Dasar to modern approaches like instance discrimination and momentum contrast. The latter, in particular, addresses the challenge of maintaining an up-to-date memory bank for effective learning. The paragraph illustrates how self-supervised learning leverages the redundancy in multimodal signals to train predictive models without external annotations.
🎲 Pretext Tasks in Self-Supervised Learning
This section explores pretext tasks used in self-supervised learning for computer vision. Pretext tasks are games or challenges that models must solve to learn about the visual world without explicit labeling. Examples include predicting the relative position of image patches, in-painting, solving jigsaw puzzles, colorization, and counting objects. The paragraph warns of the potential for models to 'cheat' by exploiting low-level signals rather than learning the intended high-level features, as seen in a study where models used chromatic aberration to solve tasks. The importance of carefully constructing pretext tasks is highlighted, as they can significantly influence the learning process. The paragraph also mentions various creative pretext tasks that have been developed for training deep neural networks effectively.
🐾 Advanced Techniques in Self-Supervised Learning
The paragraph discusses advanced techniques in self-supervised learning, focusing on instance discrimination and its extension, momentum contrast. Instance discrimination trains a model to uniquely encode each image instance, while momentum contrast addresses the issue of stale memory banks by using a queue of recent samples and a momentum encoder. The paragraph also touches on specialized downstream tasks like learning tracking models from colorization in videos and detecting object keypoints without supervision through viewpoint factorization. The innovative approaches demonstrate how self-supervised learning can be adapted to specific tasks beyond general image representation.
🏷️ Pseudo-Labeling in Semi-Supervised Learning
The final paragraph shifts focus to pseudo-labeling within the realm of semi-supervised learning. Pseudo-labeling involves training a classifier on labeled data, using it to predict labels for unlabeled data (pseudo-labels), and then retraining the classifier on these pseudo-labels. The process can be iterated for improved performance. The paragraph provides an example of pseudo-labeling in word sense disambiguation and its effectiveness in large-scale image classification using ImageNet and JFT-300M datasets. The 'noisy student' technique is highlighted for its significant gains over traditional training methods. The paragraph concludes by emphasizing the growing value of pseudo-labeling as manual annotation struggles to keep pace with the vast amounts of sensory data being generated.
Mindmap
Keywords
💡Self-supervised learning
💡Pseudo-labeling
💡Multi-modal learning
💡Benchmarks
💡Human perception system
💡Redundancy in sensory signals
💡Pretext task
💡Instance discrimination
💡Momentum contrast (MoCo)
💡Contextual redundancy
Highlights
Self-supervised learning and pseudo-labeling are key areas of focus in advancing machine perception.
Supervised learning has made significant progress in machine perception but still has limitations.
Human perception development provides inspiration for improving machine learning.
Human babies learn incrementally in a dynamic environment with multi-modal experiences.
The challenge of embodied learning is highlighted by Alan Turing's observations from 1948.
Self-supervised learning aims to create its own supervision, inspired by human multi-modal learning.
Redundancy in sensory signals is crucial for learning, as noted by Barlow.
Predictive models can be trained using redundant signals to provide labels for learning.
Self-supervised learning can be operationalized by learning from redundant signals across modalities.
Modern self-supervised learning methods often involve pretext tasks that provide a context for learning.
Pseudo-labeling is a semi-supervised learning technique that uses predictions to train on unlabeled data.
Instance discrimination is a powerful mechanism for self-supervised learning.
Momentum Contrast (MoCo) is an extension to instance discrimination that addresses memory bank issues.
Self-supervised learning has been applied to specialized tasks like tracking and keypoint detection.
Pseudo-labeling has been successfully applied to large-scale image classification.
The effectiveness of pseudo-labeling is underscored by its ability to leverage unannotated data.
The future of pseudo-labeling is promising as manual annotation struggles to keep up with data creation.
Transcripts
good day everyone
in this video i will aim to provide a
brief digest of material on
self-supervised learning and
pseudo-labeling
this material forms part of some
lectures i gave in 2021 as part of the
four f-12 lecture series at the
university of cambridge
to provide a brief outline for the video
we will start out with self-supervised
learning before moving on to
pseudo-labeling
we'll start out with the first topic
let's begin with the motivation for
self-supervised learning starting with a
summary of the state of the nation for
the world of machine perception
we have a number of reasons to be
cheerful
deep learning has given us remarkable
progress with the supervised learning
paradigm in which we gather large
collection of data and manually annotate
it and supervise a model with the
resulting annotated data
this has yielded truly major gains on
vision benchmarks
of course
benchmarks are not the goal in
themselves and they have drawbacks they
can trap you into a local minimum of
research ideas
but they have clear value in allowing an
objective comparison of different
methods
still
we have some cause for concern
and somehow it seems that we might still
have a long way to go
even the highest capacity models trained
on the largest annotated data sets
continue to make what we might call
silly mistakes mistakes that would be
never made by a human
perhaps more worryingly it seems that we
can just never get enough label data to
get close to the human perception system
this state of affairs prompts a natural
age-old question
can we take inspiration from the early
stages of development of human
perception to improve things
to answer this it's worth considering
the wealth of research that has studied
human development in some detail
human baby learning is incremental a
child learns in a continuously evolving
environment rather than a stationary
distribution
social
babies learn from other humans around
them particularly caregivers
physical
they offload knowledge to the physical
world around them and store information
with respect to their surroundings
exploratory once their curiosity is
aroused they try a lot of things to try
to find something that works
learning is language based allowing them
to not only communicate but also learn
abstractions that support generalization
finally their learning is highly
multi-modal experiencing sensations from
sight sound touch taste proprioception
balance and smell when these senses are
available
different modalities provide significant
redundancy among their inputs to learn
from
it's also interesting that babies
naturally build curricula for their
learning
careful analysis of the appearance of
objects observed by babies shows that
they follow a strong power law
a small number of objects are seen an
incredibly large number of times
such that the child becomes an expert in
recognizing and manipulating those
objects
okay this all sounds great and many
people have observed that our current
machine perception systems are doing
very little of these human inspired
learning strategies
why is that
i would say that the main barrier to
implementing the kind of embodied
perception learning exhibited
in babies has been practical
in fact this challenge was noticed by
alan turing who was interested in the
development of such machines and
observed in 1948 that
in order that the machine should have a
chance of finding things out for itself
it should be allowed to roam the
countryside and the danger to the
ordinary citizen would be serious
the takeaway here is that there is some
practical challenge to
embodied learning which is the general
term given to an agent possessing a body
that learns to interact with its
environment
it's possible that recent developments
in simulation may help us here but we're
still some way away from creating the
world in sufficient fidelity to have
fully addressed this problem
for that reason we will talk about a
family of methods that tries to make
progress principally on perhaps the
safest of the human learning
characteristics multi-modal learning
in particular we will discuss
self-supervised methods that are partly
inspired by human multimodal learning in
the sense of exploiting redundant signal
the essence of self-supervised learning
as you might infer from the name is that
the learner should create its own
supervision
this is an old idea that was nicely
articulated by helmholtz in his
legendary 1878 speech on the facts in
perception each movement we make by
which we alter the appearance of objects
should be thought of as an experiment
designed to test whether we have
understood correctly the invariant
relations of the phenomena before us
that is their existence in definite
spatial dimensions
so we can create a plentiful supply of
learning targets by simply moving and
checking whether our model of the world
was able to predict what we will see
the role of redundancy in sensory
signals was then considered more
explicitly by barlow who made the
observation that learning requires
previous knowledge
in particular to detect a new
association such as the event c
preceding event u
we need to know the prior probabilities
of c and u
if we have those then we can learn a new
association if we observe that c is
followed by u more frequently than would
be expected by chance
the key role of redundancy is that in
order to know what usually happens we
need redundancy in the input signal
which could be for example sensory
messages of the same event from
different modalities
the redundant signal is by definition
the part of the signal that can be
predicted from the remaining signal
this redundant signal provides labels
for training a predictive model
one computational trick that is nicely
illustrated in barlow's work relates to
the kinds of codes we can use to
represent events in our environment
the observation is the following
to test whether two events are
co-occurring more frequently than would
happen by chance we need to know their
prior joint probabilities
so when learning pairwise associations
between n events we need to store
n-squared co-occurrence probabilities
but if we've learned representations in
which events c and u are statistically
independent we can compute the chance
co-occurrence of c and u from the
product of their marginals that means we
only need to store n event probabilities
this can help to avoid a combinatorial
explosion of storage for prior event
probabilities
barlow himself suggested minimum entropy
coding as a scheme to obtain factorial
representations
but this idea applies more generally
it's always desirable to achieve this
property where possible
one of the first works to use the term
self-supervised that operationalized
this insight about learning from
redundant signal was proposed by
virginia dasar in the 1990s
who helpfully provided an intuitive
bovine based explanation
in supervised learning each time we see
an image of a cow as input
we are providing the corresponding cow
label
but it is implausible to collect all
labels required for such a task
unsupervised learning takes in the same
cow image and seeks to learn a powerful
representation of it without labels
in the self-supervised approach proposed
here
labels are derived from co-occurring
inputs across modalities providing
redundant signal
learning then proceeds by minimizing the
disagreement between class labels
predicted from each modality rather than
matching predictions against an external
annotation set
this is an idea that underlies a number
of modern approaches
it's also worth noting that in the
modern literature the distinction
between self-supervised and unsupervised
learning as given here can become a
little blurry
a general way to think about the
redundancy found in multimodal signals
is that the redundancy comes from the
context surrounding a piece of
information
in the natural language processing
community
unlabeled text corpora have been used
for a long time to provide low level
supervision for neural networks
with the hope that the distributed
representations learned by these models
will enable them to generalize
one example of such models are auto
regressive models in which the joint
probability of a sequence is factored
into a product of conditional
distributions
with each element in the sequence
conditioned on the previous elements
then
a network can be trained to maximize the
likelihood of a text corpus under this
factorization
for example
a network can be trained to predict the
next character in text to enable
compression
or to predict the next word to learn a
language model
a slightly more adventurous use of
context was explored by the word to vec
skip graham model which was trained to
predict the collection of words
surrounding any given word in the corpus
this is simple to train without labels
you simply pick a word in a sentence
mask its neighbors and then try to
predict those neighbors from the current
word
this work was really a breakthrough in
terms of language modelling performance
and highlighted the critical importance
of having lots of training data in
getting good word vectors
more recently various multitask masking
schemes have been considered
perhaps the best known is burt which was
trained to predict randomly masked words
in a sequence in addition to predicting
the next sentence in a corpus
this work showed amongst other things
the benefits of using a high capacity
transformer for language modeling
it's perhaps a little less obvious how
the same approach can be applied to
computer vision
the strategy that has been developed has
been to train a neural network by
tasking it with playing a game
which is often referred to as a pretext
task
now typically we don't care about the
performance of the model on the pretext
task itself but we hope that by solving
it a model learns good representations
of the visual world
work by carl dusch and colleagues
illustrates this idea nicely
a network is shown two image patches
these image patches have been cropped
from nearby regions in the same image
the pretext task for the model is to
guess the relative position of the red
dashed cropped to the blue crop
here's an example
ask yourself where does the red dashed
cropped belong to relative to the blue
crop
here's a second example again where does
the red dash crop belong to relative to
the blue crop
when you worked out that the answer to
question one was probably bottom right
and question two was probably top middle
you made use of your knowledge of buses
and trains despite almost certainly
having never seen this particular bus or
this particular train before
the key idea is that a model can only
solve these questions once it learns
about cats buses and trains and
importantly no labeling is required to
construct this task
this seems like a cute idea and it is
but a warning is needed
sometimes the model won't solve the task
in the way that you want it
in this work dorse et al found that the
network can learn to cheat by exploiting
a low level signal the chromatic
aberration that results from a camera
lens focusing different light
wavelengths differently
one color typically green is shrunk
towards the image center relative to the
others
once the network has figured this out
it can solve the problem trivially by
determining the absolute locations of
the patches relative to the lens without
learning anything at all about cats
buses trains or the host of other
interesting objects we'd like it to know
about
the authors solved this problem by
randomly dropping colour channels from
each patch so that the network could not
rely on this queue
but still it provides a cautionary tale
constructing pretext tasks requires a
great deal of care
partly because it works so well and
partly because it's a fun research
problem
researchers have come up with a number
of creative pretext tasks for training
deep neural
networks some examples include training
a network to in-paint by removing
patches of images and requiring the
model to fill them in
requiring the network to solve jigsaw
puzzles by giving it a shuffled set of
patches and asking it to rearrange them
to match an image
colorization in which a model is given
an image whose colour has been removed
and then tasked with predicting the
original colour version
there is training a model to count this
is a slightly ingenious idea the network
is given image patches and has to count
objects in those patches in such a way
that when applied to the full image the
total object counts match the sum of the
object counts across each patch
other work has trained a model to invert
again by training a second gan to
generate the latent codes of the
original gan
one particularly appealing pretext task
is to train a model to group pixels
according to optical flow
the idea here is to train a model that
learns pixel embeddings that are similar
if and only if those pixels tend to move
with the same velocity in videos which
is a way of encoding the prior knowledge
that the pixels on a common object tend
to move together
clustering has also proven to be highly
effective here a network alternates
between clustering its own feature space
and classifying the cluster membership
of each image
finally rotation prediction can be used
to exploit human photographer bias
we tend to photograph things the right
way up so we can rotate images and ask
the model to predict the rotation that
has been applied
this is also surprisingly powerful
i'd like to talk in a little more detail
about a task formulation that has
emerged as one of the most powerful
mechanisms for self-supervised learning
namely instance discrimination
one motivation for considering this idea
stems from the observation that despite
training with semantic labels
fully supervised convolutional neural
networks also appear to capture visual
similarity between instances
given an input image of a leopard
the classification scores of a fully
supervised model are strongest for the
class of leopard
but also for highly visually similar
classes like jaguar and cheetah and much
less so for glasses like lifeboat shop
cart and bookcase
an interesting question is then whether
the same property would emerge if we
trained a model to discriminate between
individual instances rather than
semantic classes
one work from wu at al explored this
idea
it trained a cnn to map all image
instances of a data set
to 128 dimensional vectors storing them
in a data structure called a memory bank
the model was trained to encourage each
instance to be mapped to a different
location on a 128 dimensional unit
sphere such that each image when encoded
with the cnn would uniquely retrieve its
memory bank vector
this model can be trained without labels
but empirically nevertheless learns
strong image representations
momentum contrast considered an
extension to this approach
the motivation behind this work was that
instance discrimination works well but
memory banks have an issue
on the one hand recomputing the features
stored in the bank ie one feature per
image in the data set for every update
to the cnn parameters would be
prohibitively expensive
on the other if memory bank instances
are not regularly updated they grow
increasingly stale with every
optimization step
which is sub-optimal for instance
discrimination
moco or momentum contrast aims to avoid
this stay on this issue by first
replacing the memory bank with a queue
of recently encoded samples fewer than
the full data set
and encoding queue samples with a
momentum encoder which is formed from a
slow moving average of query encoder
weights
moco uses some additional terminology
keys refer to instances encoded in the
queue with the momentum encoder
queries are instances to be compared
against keys
positive pairs are queries and keys
originating from the same image and by
extension negative pairs are queries and
keys originating from different images
the instance discrimination task is then
to match up queries against the keys
that represent their positive pairs
using what's known as an info nce loss
which is essentially a standard
cross-entropy softmax
finally the resulting query encoder then
provides a useful representation for
downstream tasks
the methods we've discussed so far have
focused principally on learning general
image representations
however the ideas behind self-supervised
learning have also been applied to
tackling more specialized downstream
tasks
one lovely piece of work by vondrick and
collaborators showed how to learn a
tracking model by performing
colorization
the key idea is to use colors across
unlabeled videos as a source of
supervision
the model is given a black and white
frame that it needs to colorize and a
reference black and white frame
at each location in the input frame it
is tasked with pointing to the location
in the reference frame that contains the
same color as the current location
this color is copied across to the input
frame from the color version of the
reference frame so that a loss can be
applied at the same place in the true
colors of the current frame
in practice this can be implemented by
training a cnn that produces a low
dimensional embedding at each location
of an image then performing pointing
from the target frame to the reference
frame by simply comparing the
similarities of the embeddings at each
location in each frame
given enough data the model indeed
learns to solve this task and the
authors show that without any labels it
gains the ability to track objects
across frames
another direction has considered
learning object key points without
supervision
the idea shown here for cat faces was to
learn a model that would learn to detect
consistent locations on an object
without any labels
the approach here was to use a concept
called viewpoint factorization
the essence of this idea is if you had a
good key point detector it should fire
on the same point of the cat's face even
as the cat's face moves around
in practice that can be encouraged by
enforcing what's known as equivariance
as the image is translated the key point
is also translated by the same amount
since we have no annotations just images
of cat faces these translations and
other geometric transformations are
generated via synthetic image warps and
a loss is used to ensure that the key
points move consistently with the warps
another direction has sought to learn
powerful video representations with a
simple idea
a model takes in several video clips one
of which has had its frames shuffled
training a model to predict which clip
was shuffled learns representations that
are particularly useful for action
recognition
we now turn to sudo labeling
i'd like to talk briefly about
semi-supervised learning and the
pseudo-labeling algorithm
semi-supervised learning considers the
setting in which a learner assumes
access to both labeled and unlabeled
data during training
typically to provide benefit the
unlabeled data is assumed to be
significantly larger than the label data
pseudo-labeling which is sometimes also
referred to as self-training or
self-labeling is a term that refers to
some variation of the following
algorithm
first a classifier is trained on the
labeled data
next the classifier is used to predict
the labels of the unlabeled data these
labels are referred to as pseudolabels
the classifier is then retrained on the
pseudo labels
often this process is iterated by
regenerating a new set of pseudo labels
retraining generating new pseudo labels
etc a nice illustration of this
algorithm is provided by the work of
yarovsky
who focused on the task of word sense
disambiguation across a full corpus
here the task is to determine the sense
in which a word is meant
for example the word plant may refer to
a manufacturing plant or a live plant
the algorithm proceeds by obtaining an
initial small collection of labelled
samples
and then using them to train a
classifier
then it predicts labels for unlabeled
sequences
keeping those that have high confidence
and optionally filtering and expanding
the labeled sets via some nlp heuristics
this stage is then repeated until
convergence to a final state
the reason for mentioning this algorithm
is twofold
one it's a pleasingly simple algorithm
two
when large quantities of data are
available it works remarkably well in a
wide range of settings
david yarovsky awkwardville's work noted
that it thrives on unannotated
monolingual corpora the more the merrier
as an example application in computer
vision pseudo-labeling was applied to
improving large-scale image
classification performance by taking
imagenet as a source of labelled images
and jft 300 million as a source of
unlabeled images
the method referred to as noisy student
trained an initial model on the labelled
data inferred pseudolabels and then
retrained higher capacity model on the
pseudo-labeled data
making heavy use of data augmentation
before repeating
noisy student led to significant gains
over image net only training
highlighting the effectiveness of this
technique
more broadly i think that pseudo
labeling algorithms are likely to prove
increasingly valuable in future as
manual annotation simply cannot keep up
with the scale of sensory data sets that
are now being created
we've reached the end
thank you for your attention
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