DeiT - Data-efficient image transformers & distillation through attention (paper illustrated)
Summary
TLDRThe script discusses 'Data-Efficient Image Transformer' (DEIT), a breakthrough in training transformers for image tasks with limited data. It highlights DEIT's superior performance over Vision Transformer (ViT), requiring less data and compute power. The paper introduces knowledge distillation techniques, using a teacher network to enhance the student model's learning. DEIT's architecture, including class and distillation tokens, is explained, along with the effectiveness of hard distillation and various data augmentation strategies. The summary emphasizes the importance of a high-quality teacher network and the potential of standalone transformer architectures in the future.
Takeaways
- đ The paper 'Data-Efficient Image Transformer' (DEIT) shows it's feasible to train transformers on image tasks with less data and compute power than traditional methods.
- đĄ DEIT introduces knowledge distillation as a training approach for transformers, along with several tips and tricks to enhance training efficiency.
- đ DEIT outperforms Vision Transformer (ViT) significantly, requiring less data and compute power to achieve high performance in image classification.
- đ DEIT is trained on ImageNet, a well-known and much smaller dataset compared to the in-house dataset used by ViT, making it more practical for limited data scenarios.
- đïž Knowledge distillation involves transferring knowledge from a 'teacher' model to a 'student' model, with the teacher providing guidance to improve the student's learning.
- đ„ A key feature of DEIT is the use of 'distillation tokens' from the teacher network, which, when combined with class tokens, leads to improved performance.
- đ The teacher network in DEIT is a state-of-the-art CNN pre-trained on ImageNet, chosen for its high accuracy to enhance the student model's learning.
- đ DEIT employs various data augmentation techniques such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix to improve model robustness.
- đ§ Regularization techniques are used in DEIT to reduce overfitting and ensure the model learns the actual information from the data rather than noise.
- đ The paper includes ablation studies that demonstrate the effectiveness of distillation tokens and the various augmentation strategies used in training DEIT.
- đ DEIT represents a significant advancement in making transformer models more accessible and efficient for image classification tasks with limited resources.
Q & A
What is the main contribution of the 'Data-Efficient Image Transformer' (DEIT) paper?
-The DEIT paper introduces a practical approach to train transformers for image tasks using distillation, and it provides various tips and tricks to make the training process highly efficient. It demonstrates that DEIT outperforms Vision Transformer (ViT) with less data and compute power.
How does DEIT differ from the original Vision Transformer (ViT) in terms of training dataset size?
-ViT was trained on a massive in-house dataset from Google with 300 million samples, while DEIT is trained using the well-known ImageNet dataset, which is 10 times smaller.
What is the significance of knowledge distillation in the context of DEIT?
-Knowledge distillation is a key technique in DEIT where knowledge is transferred from a pre-trained teacher network (a state-of-the-art CNN on ImageNet) to the student model (a modified transformer), enhancing the student model's performance with less data.
How does the distillation process in DEIT differ from traditional distillation?
-In DEIT, the distillation process uses a state-of-the-art CNN as the teacher network and employs hard distillation, where the teacher network's label is taken as the true label, rather than using a temperature-smoothed probability.
What is the role of the 'temperature parameter' in the softmax function during distillation?
-The temperature parameter in the softmax function is used to smoothen the output probabilities. A lower temperature makes the probabilities more confident, while a higher temperature makes them more uniform.
What are the different variations of DEIT models mentioned in the script?
-The script mentions DEIT-ti (a tiny model with 5 million parameters), DEIT-s (a small model with 22 million parameters), DEIT-b (the largest model with 86 million parameters, similar to ViT-b), and DEIT-b-384 (a model fine-tuned on high-resolution images of size 384x384).
Which teacher network does DEIT use, and why is it significant?
-DEIT uses a state-of-the-art CNN from the NeurIPS 2020 paper with the highest accuracy on ImageNet. The better the teacher network, the better the trained transformer will perform, as it transfers knowledge effectively.
What are some of the augmentation and regularization techniques used in DEIT to improve training?
-DEIT employs techniques such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix to create multiple samples with variations and reduce overfitting.
How does the script describe the effectiveness of using distillation tokens in DEIT?
-The script indicates that distillation tokens, when used along with class tokens, bring better accuracy compared to using class tokens alone, although the exact contribution of distillation tokens is still to be fully understood.
What are the implications of the results presented in the DEIT paper for those with limited compute power?
-The results suggest that DEIT can produce high-performance image classification models with far less data and compute power compared to ViT, making it a practical solution for those with limited resources.
What does the script suggest about the future of standalone transformer architectures?
-The script suggests that while DEIT relies on a pre-trained teacher network, the community is eagerly waiting for a standalone transformer architecture that can be trained independently without depending on other networks.
Outlines
đ Introduction to Data-Efficient Image Transformers (DEIT)
The first paragraph introduces the concept of Data-Efficient Image Transformers (DEIT), a breakthrough in training transformer models on image data with significantly reduced data and computational resources. The DEIT approach is contrasted with the Vision Transformer (ViT), which requires a massive dataset and substantial computational power. DEIT's efficiency is attributed to a novel training method involving knowledge distillation, regularization, and augmentation techniques. The paragraph also emphasizes the practicality of DEIT for those with limited compute resources, highlighting its ability to achieve high performance with a fraction of the data and training time compared to ViT.
đ Understanding DEIT's Training Techniques and Model Architecture
This paragraph delves into the specifics of DEIT's training techniques, focusing on knowledge distillation as a key method for transferring knowledge from a pre-trained teacher network to a student model. It explains the process of distillation, including the use of a temperature parameter to smoothen output probabilities and the concept of hard distillation where the teacher network's label is used as the true label. The paragraph also discusses the architecture of DEIT, including the use of class tokens, patch tokens, and distillation tokens, and how these elements contribute to the model's improved performance. Additionally, it mentions the different variations of the DEIT model, such as DEIT-ti, DEIT-s, and DEIT-b, each with varying parameters and capabilities.
đ The Impact of Teacher Networks and Training Strategies on DEIT's Success
The final paragraph discusses the importance of the teacher network in the DEIT framework, revealing that a state-of-the-art convolutional neural network pre-trained on ImageNet is used. It underscores the correlation between the quality of the teacher network and the performance of the trained transformer. The paragraph also reviews the effectiveness of various augmentation and regularization strategies employed during training, such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix, which are crucial for achieving DEIT's impressive results. It concludes with an acknowledgment of the audience's patience and an invitation for feedback, reflecting the community-driven nature of the discussion.
Mindmap
Keywords
đĄTransformer
đĄData-Efficient Image Transformer (DEIT)
đĄKnowledge Distillation
đĄRegularization
đĄAugmentation
đĄVision Transformer (ViT)
đĄTeacher Network
đĄStudent Model
đĄCross-Entropy Loss
đĄTemperature Parameter
đĄAblation Study
Highlights
Training transformers on images is practical, as shown by the Data-Efficient Image Transformer (DEIT).
DEIT proposes knowledge distillation as a training approach for transformers.
The paper provides tips and tricks to make transformer training efficient.
DEIT outperforms Vision Transformer (ViT) with less data and compute power.
ViT requires a massive dataset from Google, while DEIT uses the smaller ImageNet.
DEIT's training time is significantly reduced to two to three days on a 4-8 GPU machine.
Understanding DEIT requires knowledge of distillation, regularization, and augmentation.
Distillation involves transferring knowledge from a teacher network to a student model.
Regularization aims to reduce overfitting and improve model generalization.
Augmentation creates varied samples from the same input to enhance model robustness.
DEIT uses a modified distillation approach with a pre-trained CNN as the teacher network.
The student architecture in DEIT is a modified transformer that incorporates CNN outputs.
Hard distillation is used in DEIT, taking the teacher network's label as the true label.
DEIT's architecture includes class tokens, patch tokens, and distillation tokens.
Experiments show that distillation tokens significantly improve DEIT's performance.
DEIT comes in various sizes: DEIT-ti, DEIT-s, DEIT-b, and DEIT-b/384 for different needs.
The teacher network used in DEIT is a state-of-the-art CNN from the NeurIPS 2020 paper.
Better teacher networks lead to better trained transformers in DEIT.
Distillation is more effective than soft distillation, achieving higher accuracy.
DEIT's success relies on various augmentation and regularization strategies.
Repeat augmentation, auto augment, rand augment, mix-up, and cut mix are among the techniques used.
The paper's ablation studies reveal the contribution of each technique to DEIT's performance.
The standalone transformer architecture trained independently is still awaited.
Transcripts
training data efficient
image transformer or date in short is
one of the first papers to show that
it is practical to train transformers
for tasks
on images the paper not only proposes
distillation as a training approach to
train transformers
but also provides a bunch of tips and
tricks to make the training
super efficient the paper shows
a straight comparison of vision
transformer with date
and clearly date outperforms vision
transformer by a good margin
not only that date requires far less
data
and far less compute power to produce a
high performance image classification
model
for those of us who only have limited
compute power
and are wondering how to train the
wisdom transformer on a custom data set
date is the answer let's learn about
date in this video
[Music]
vision transformer or vit was the first
paper
which showed that transformers can be
used for computer vision tasks
it trained on a massive data set of 300
million samples
and the data set is an in-house data set
from google
and it's not it available to download on
the other hand
date is trained only using the
well-known imagenet
which is a 10 times smaller data set
because of the massive dataset size
vision transformer
needs extensive compute power for
training making it
impractical to train models in the
limited data regime
on the other hand the training time for
date is two to three days
on a single 4 gpu or 8 gpu machine
now this is an impressive leap in
performance so let's delve
deeper and try to understand date much
better
to understand date we need to know
distillation
regularization and augmentation
so knowledge distillation is when you
transfer knowledge
from one model or network to another
network
by some means regularization
is when you try to reduce overfitting of
a network
to given limited training data so that
your model
does not learn the noise in the data but
the actual
information from the data augmentation
is when we create multiple samples of
the same input
with some variations though these are
some of the techniques used in date the
key contributor
is distillation so let's recap
distillation first
and see how it is used in this paper
let's say we have a neural network in
classic machine learning setting
that recognizes cats and dogs to train
this network
we first pass the cat image through the
model and get the representation
or embeddings of the image the embedding
is then passed through
a softmax function to get the
probabilities
for the input classes doc and cad
we then compute a cross entropy loss
comparing
with the ground with labels and train
the entire network
with distillation we distill the
knowledge from another network
called the teacher network or the
teacher model
we first get the embeddings from the
teacher network
we now pass it through a softmax with a
special
temperature parameter tau to get the
output probabilities
the significance of temperature is to
smoothen
the output probabilities for instance if
the softmax function
says the probability of cat is 0.9
with the temperature set to 0.5 it only
says the probability of cat
as 0.7 with the output of the teacher
network
we compute a distillation loss between
the teacher output
and the ground truth and sum the loss
with the cross
entropy loss of our student model in
order to join the
student model
date proposes a modified version of
distillation approach we just saw
the teacher network that they use is a
state-of-the-art convolutional neural
network
that is pre-trained on imagenet the
student architecture
is the modified version of transformer
the main modification is that the output
of the cnn
is also passed as an input to the
transformer
while computing the distillation loss we
do what is called the hard distillation
where the temperature is equal to one
what it means is that we literally take
the label of the teacher network
as the true label we then sum up this
distillation loss
with a cross entropy of the transformer
and trying the transformer
[Music]
now with that information if we take a
look at this figure from the paper
i think we gain a better understanding
of date so the class tokens and patch
tokens is the same as the vision
transformer
they are put through several layers of
attention and we obtain the classes
on top of that we also have the
distillation tokens
which are complementary to the class
tokens but come from the teacher network
the authors have experimented and shown
in the paper that
distillation token trained with a
teacher token
is what provides the impressive
improvement in performance
they've also experimented with several
variations of this data architecture
date ti is a tiny model with 5 million
parameters
date s is a small model with 22 million
parameters
date b is the largest model and is the
same as vision transformer b
with 86 million parameters
date b 384 is the model fine tuned on
high resolution
training images of size 384 by 384
and finally the date with this symbol
stands for the proposed distillation
procedure
[Music]
now i've been mentioning about the
teacher network which is a convolutional
neural network
but which network do they use the answer
is that they use a state-of-the-art
network proposed in this new ribs 2020
paper
and they went for the biggest 16 gf
model which has the highest accuracy of
82.9 on imagenet
why because the better the teacher
network
the better our trained transformer will
be
as can be seen from the results heart
distillation
seems to be quite effective compared to
soft distillation
as it reaches an accuracy of 83 percent
that is not possible otherwise we can
also observe that the distillation
tokens bring
better accuracy when used along with
class tokens
instead of just using the class tokens
as in the vision transformer
lastly increasing the training ebox and
training for longer time
somehow seems to be more effective when
it comes to transformers
[Music]
until this paper training a transformer
on images wasn't easy
so they should have adapted quite a few
tricks and strategies
to train the transformer successfully
this table
summarizes some of the augmentation and
regularization
tricks that they used in order to arrive
at the impressive results proposed let's
briefly
look at each of them
repeat augmentation is when we first
augment images in a batch
and use all the images together in this
simple example
of a batch of a dog and a cat
we augment them and make a batch of four
images instead of two
auto augment is when you search for the
best augmentation policy
for the given data rather than manually
defining
some augmentations irrespective of the
data
rand augment can be implemented in two
lines of code
as shown here the idea is that you
randomly choose
n augmentations from a pool of
augmentations
and simply use the chosen ones
random erasing is super easy too you
randomly erase a rectangular patch in
the input image
and use the erased image in case of
mix-up you add up or do some arithmetic
of the inputs
to arrive at a new training sample
in cut mix you cut out a region from a
given
input and stick in another sample with
different label
and modify the label accordingly
so to summarize these are the tricks
that that were experimented in the paper
and in this ablation studies table they
present the results of use
using each of them while the paper says
that distillation tokens
bring something to the table we are yet
to figure out what exactly it is that
contributes to the better performance
clearly it's bringing something quite
different from the class tokens
also let's not forget that the teacher
network used here
is already trained on imagenet
so clearly we have to wait to see a
standalone transformer architecture
trained independently without depending
on any other networks
while we eagerly wait for that
transformer architecture
i would like to thank you so much for
watching patiently
till the end and supporting the
community by
leaving your comments below thank you
very much
you
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