ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases (2103.10697v2)

Abstract: Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling. Vision Transformers (ViTs) rely on more flexible self-attention layers, and have recently outperformed CNNs for image classification. However, they require costly pre-training on large external datasets or distillation from pre-trained convolutional networks. In this paper, we ask the following question: is it possible to combine the strengths of these two architectures while avoiding their respective limitations? To this end, we introduce gated positional self-attention (GPSA), a form of positional self-attention which can be equipped with a ``soft" convolutional inductive bias. We initialise the GPSA layers to mimic the locality of convolutional layers, then give each attention head the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information. The resulting convolutional-like ViT architecture, ConViT, outperforms the DeiT on ImageNet, while offering a much improved sample efficiency. We further investigate the role of locality in learning by first quantifying how it is encouraged in vanilla self-attention layers, then analysing how it is escaped in GPSA layers. We conclude by presenting various ablations to better understand the success of the ConViT. Our code and models are released publicly at https://github.com/facebookresearch/convit.

An Analysis of "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases"

The paper "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases" addresses the integration of convolutional neural network (CNN) features within vision transformers (ViTs). Authored by Stéphane d’Ascoli et al., this work introduces a novel architectural method to meld convolutional inductive biases with self-attention mechanisms, enhancing both the performance and sample efficiency of ViTs.

The authors propose the concept of gated positional self-attention (GPSA). This mechanism enables self-attention layers to incorporate a "soft" convolutional inductive bias, thus balancing the strengths of ViTs and CNNs while mitigating their individual limitations. The GPSA layers are initialized to emulate the locality typical of convolutional layers. They offer each attention head the flexibility to diverge from purely local attention via a gating parameter that tunes the emphasis on positional versus content information.

Key Contributions and Methodology

1. Introduction of GPSA Layers:
   • The GPSA layers are designed to integrate both positional and content information. Initially, these layers mimic the behavior of convolutional layers, providing local attention. Over the course of training, each attention head can adjust its gating parameter, thus extending its focus based on the context.
   • The incorporation of adaptive attention spans mitigates the problem of an excessive number of trainable parameters, which typically arises from self-attention mechanisms.
2. Performance Evaluation:
   • The authors evaluated various configurations of their model, dubbed ConViT, against the DeiT baseline across different data regimes, particularly using ImageNet and CIFAR100 datasets.
   • Obtained results indicated that ConViT outperforms DeiT models of equivalent sizes and computational requirements not only in terms of final test accuracy but also in training efficiency.
3. Ablation Studies and Theoretical Insights:
   • Through a series of ablation experiments, the authors investigated the significance of various components like convolutional initialization and gating parameters.
   • They provided theoretical contributions by analyzing the emergent locality and the learning dynamics facilitated by GPSA layers, indicating that layers inherently benefit from localized initialization and smoothly transition to more diverse attention mechanisms.
4. Implications for Model Training:
   • Practical implications of this work include a more efficient training regime, particularly in low-data scenarios. ConViT model displayed significant improvements in sample efficiency, a critical factor for tasks with limited annotated data.
   • ConViT models exhibited a quicker convergence during early epochs, enhancing the practical feasibility of using these architectures in rapid prototyping scenarios where computational resources and time are constrained.

Results and Implications

Table 1 and Table 2 in the document compare the architecture's performances, demonstrating ConViT's superiority in both sample and parameter efficiencies compared to the DeiT models. For instance, ConViT-S achieved a top-1 accuracy of 81.3% on ImageNet, compared to DeiT-S's 79.8%, with the added benefit of improved sample efficiency, particularly in scenarios where the training dataset size is reduced.

Additionally, Figure 2 from the paper illustrates that ConViT models maintain a consistent performance edge across various model scales and data regimes, reinforcing the robustness of incorporating soft convolutional biases.

Future Directions

Recognizing the complementarity of convolutional inductive biases and self-attention mechanisms opens several avenues for future research. Potential directions include:

• Extending the gating mechanisms to incorporate more varied inductive biases beyond convolutions.
• Exploring hybrid models with smarter initialization schemes that leverage pre-trained convolutional features.
• Investigating similar principles in other domains such as NLP where locality might offer benefits in diverse contexts.

Conclusion

The paper "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases" by Stéphane d’Ascoli et al. represents an important advancement in the architecture of vision transformers. The proposed ConViT framework elegantly combines the strengths of CNNs and transformers, yielding significant improvements in performance and training efficiency without incurring additional computational costs. This work underscores the potential of blending architectural priors with flexible learning mechanisms, setting a precedent for future innovations in model architectures that bridge distinct deep learning paradigms.