Explanation on Pretraining Bias of Finetuned Vision Transformer
This work addresses interpretability challenges for researchers and practitioners using pretrained ViTs, but it is incremental as it builds on existing methods for analyzing attention patterns.
The paper tackled the problem of interpreting attention maps in Vision Transformers (ViTs) by proposing Input-Attribution and Attention Score Vector (IAV) to measure similarity between attention maps and input-attribution, revealing that IAV trends can separate supervised and unsupervised pretraining types, while generalization, robustness, and entropy of attention maps are not properties of pretraining types.
As the number of fine tuning of pretrained models increased, understanding the bias of pretrained model is essential. However, there is little tool to analyse transformer architecture and the interpretation of the attention maps is still challenging. To tackle the interpretability, we propose Input-Attribution and Attention Score Vector (IAV) which measures the similarity between attention map and input-attribution and shows the general trend of interpretable attention patterns. We empirically explain the pretraining bias of supervised and unsupervised pretrained ViT models, and show that each head in ViT has a specific range of agreement on the decision of the classification. We show that generalization, robustness and entropy of attention maps are not property of pretraining types. On the other hand, IAV trend can separate the pretraining types.