Inspecting Explainability of Transformer Models with Additional Statistical Information
Transformer becomes more popular in the vision domain in recent years so there is a need for finding an effective way to interpret the Transformer model by visualizing it. In recent work, Chefer et al. can visualize the Transformer on vision and multi-modal tasks effectively by combining attention layers to show the importance of each image patch. However, when applying to other variants of Transformer such as the Swin Transformer, this method can not focus on the predicted object. Our method, by considering the statistics of tokens in layer normalization layers, shows a great ability to interpret the explainability of Swin Transformer and ViT.
PDF AbstractTasks
Datasets
ImageNet
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Focus •
Label Smoothing •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Stochastic Depth •
Swin Transformer •
Transformer
