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Machine learning models are generally vulnerable to adversarial examples,which is in contrast to the robustness of humans. In this paper, we try toleverage one of the mechanisms in human recognition and propose a bio-inspiredclassification framework in which model inference is conditioned on labelhypothesis. We provide a class of training objectives for this framework and aninformation bottleneck regularizer which utilizes the advantage that labelinformation can be discarded during inference. This framework enables bettercompression of the mutual information between inputs and latent representationswithout loss of learning capacity, at the cost of tractable inferencecomplexity. Better compression and elimination of label information furtherbring better adversarial robustness without loss of natural accuracy, which isdemonstrated in the experiment.

Guess First to Enable Better Compression and Adversarial Robustness