DeiT - Data-efficient image transformers & distillation through attention (paper illustrated)

Summary

The 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

• 📈 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.
• 💡 DEIT introduces knowledge distillation as a training approach for transformers, along with several tips and tricks to enhance training efficiency.
• 🏆 DEIT outperforms Vision Transformer (ViT) significantly, requiring less data and compute power to achieve high performance in image classification.
• 🔍 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.
• 🕊️ Knowledge distillation involves transferring knowledge from a 'teacher' model to a 'student' model, with the teacher providing guidance to improve the student's learning.
• 🔥 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.
• 🔑 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.
• 🔄 DEIT employs various data augmentation techniques such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix to improve model robustness.
• 🔧 Regularization techniques are used in DEIT to reduce overfitting and ensure the model learns the actual information from the data rather than noise.
• 📊 The paper includes ablation studies that demonstrate the effectiveness of distillation tokens and the various augmentation strategies used in training DEIT.
• 🚀 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.