Layer rotation: a surprisingly powerful indicator of generalization in deep networks?

5 Jun 2018Simon CarbonnelleChristophe De Vleeschouwer

Our work presents extensive empirical evidence that layer rotation, i.e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance. In particular, larger cosine distances between final and initial weights of each layer consistently translate into better generalization performance of the final model... (read more)

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