![]() ![]() In our paper, we have generatedĪrtificial perturbations to our model by hot-swapping the activation and lossįunctions during the training. Of endangered corals exposed to harsh weather to the lungs of patients Karenina principle has been found in systems in a wide range: from the surface Happy families look alike, each unhappy family is unhappy in its own way. Generalizable models happy families paralleling Leo Tolstoy dictum that all Models unhappy families vary more in their representation than more The result isĪn Anna Karenina Principle AKP for deep learning, in which less generalizable Induces changes that lead to transitions to different families. Propose the alternative perturbation of deep models during their training ![]() Theories and explanations of the generalizability of these deep networks. Recent research efforts have focused on alternative ![]() We have little understanding of these internal representations, letĪlone quantifying them. This success is attributed to good internal representationįeatures that bypasses the difficulties of the non-convex optimization Download a PDF of the paper titled Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification, by Nesma Mahmoud and 4 other authors Download PDF Abstract: Image classification with deep neural networks has reached state-of-art with ![]()
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