Conclusion

To conclude our exploration of transformers in computer vision, let's summarize the main takeaways discussed throughout the course.

Attention mechanisms and inductive bias

We started our journey by exploring attention mechanisms, highlighting their role as a fundamental concept in deep learning. The crucial notion of inductive bias was introduced, illustrating the spectrum from weak to strong inductive biases within neural networks. This concept was framed by considering neural networks as graphs, emphasizing the impact of graph connectivity constraints on modeling input data.

Graph connectivity and inductive bias

Our understanding deepened as we explored how the strength of inductive bias correlates with graph connectivity. Whether confident and strong or weak in the case of fully connected networks, we examined how attention mechanisms can be integrated with inductive bias models like convolutional neural networks (CNNs). An illustrative example from natural language processing (NLP) showcased attention mechanisms in action.

Evolution of transformers in NLP

We witnessed the evolution of transformers, gradually eliminating recurrence relations and retaining attention mechanisms alongside position encoding to effectively encode sequence information in text. The parallel encoding feature in transformers proved instrumental in handling longer sequence lengths.

Formalizing attention mechanisms

The course then progressed to formalizing attention and self-attention equations, exploring matrix and individual equations. Special attention types, including channel attention and temporal attention, were detailed, each accompanied by specific sizes and attention maps. The significance of global versus local attention, as well as multihead attention, was discussed.

Applications in computer vision

We then shifted our focus to practical applications in computer vision, spanning image classification, object detection, and semantic segmentation.