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Recently, self-attention (SA) structures became popular in computer visionfields. They have locally independent filters and can use large kernels, whichcontradicts the previously popular convolutional neural networks (CNNs). CNNssuccess was attributed to the hard-coded inductive biases of locality andspatial invariance. However, recent studies have shown that inductive biases inCNNs are too restrictive. On the other hand, the relative position encodings,similar to depthwise (DW) convolution, are necessary for the local SA networks,which indicates that the SA structures are not entirely spatially variant.Hence, we would like to determine which part of inductive biases contributes tothe success of the local SA structures. To do so, we introduced context-awaredecomposed attention (CADA), which decomposes attention maps into multipletrainable base kernels and accumulates them using context-aware (CA)parameters. This way, we could identify the link between the CNNs and SAnetworks. We conducted ablation studies using the ResNet50 applied to theImageNet classification task. DW convolution could have a large localitywithout increasing computational costs compared to CNNs, but the accuracysaturates with larger kernels. CADA follows this characteristic of locality. Weshowed that context awareness was the crucial property; however, large localinformation was not necessary to construct CA parameters. Even though nospatial invariance makes training difficult, more relaxed spatial invariancegave better accuracy than strict spatial invariance. Also, additional strongspatial invariance through relative position encoding was preferable. Weextended these experiments to filters for downsampling and showed that localitybias is more critical for downsampling but can remove the strong locality biasusing relaxed spatial invariance.

Studying inductive biases in image classification task