Conditional Networks
In this work we tackle the problem of out-of-distribution generalization through conditional computation. Real-world applications often exhibit a larger distributional shift between training and test data than most datasets used in research. On the other hand, training data in such applications often comes with additional annotation. We propose a method for leveraging this extra information by using an auxiliary network that modulates activations of the main network. We show that this approach improves performance over a strong baseline on the Inria Aerial Image Labeling and the Tumor-Infiltrating Lymphocytes (TIL) Datasets, which by design evaluate out-of-distribution generalization in both semantic segmentation and image classification.
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INRIA Aerial Image Labeling
