Can Cross-Layer Transcoders Replace Vision Transformer Activations? An Interpretable Perspective on Vision
Summary: Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoencoders (SAEs) have been used to extract human-interpretable features, they operate on individual layers and fail to capture the cross-layer computational structure of Transformers, as well as the relative significance of each layer in forming the last-layer representation.
Introduction
Recent advancements in artificial intelligence have led to the development of Vision Transformers (ViTs), which have revolutionized image recognition tasks. However, one of the prevailing challenges is understanding their internal mechanisms, specifically the activations that occur at various layers. This knowledge is essential for enhancing the interpretability of AI models, which is particularly important in applications requiring trustworthiness and transparency.
Challenges with Sparse Autoencoders
Sparse Autoencoders (SAEs) have been a popular choice for extracting features that can be interpreted by humans. However, they primarily focus on individual layers and lack the ability to encapsulate the intricate cross-layer dynamics that characterize Transformers. This limitation hinders a comprehensive understanding of how each layer contributes to the final output, making it difficult to ascertain the model’s decision-making process.
Introducing Cross-Layer Transcoders
To address these shortcomings, researchers have introduced Cross-Layer Transcoders (CLTs) as a novel approach to understanding ViTs. CLTs serve as reliable, sparse, and depth-aware proxy models for the Multi-Layer Perceptron (MLP) blocks found in ViTs. The architecture employs an encoder-decoder scheme, which reconstructs post-MLP activations from learned sparse embeddings of previous layers. This method provides a linear decomposition that transforms the opaque embeddings of ViTs into an additive, layer-resolved format.
Methodology and Training
In their study, researchers trained CLTs on the CLIP models ViT-B/32 and ViT-B/16, utilizing datasets such as CIFAR-100, COCO, and ImageNet-100. The results demonstrated high reconstruction fidelity for post-MLP activations, while also showcasing that the use of CLTs could maintain or enhance the classification accuracy of CLIP in zero-shot scenarios.
Interpretability and Findings
One of the most significant contributions of this research is the interpretability aspect of CLTs. By analyzing cross-layer contribution scores, researchers were able to provide faithful attributions that reveal the importance of different layers in the final representation. They found that the representation is largely concentrated in a few dominant layer-wise terms; removing these terms significantly degrades performance, whereas retaining them largely preserves it.
Conclusion
The findings underline the potential of Cross-Layer Transcoders as a viable alternative for interpreting Vision Transformers in the vision domain. By offering a clear, layer-wise breakdown of activations, CLTs enable a deeper understanding of model behavior, paving the way for the development of more interpretable and trustworthy AI systems.
Future Directions
As the field continues to evolve, further exploration into the integration of CLTs with other model architectures and applications will be crucial. This research sets a foundation for future studies aimed at enhancing the interpretability of complex AI models, ultimately leading to more reliable and accountable AI technologies.