We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data, in spite of overly- expressive ...
Apr 23, 2017 · In this paper, the authors claims "we believe that DNNs first learn and then refine simple patterns, which are shared across examples, in order to quickly drive ...
Feb 18, 2017 · This paper claims that DNNs don't memorize the dataset and found that DNN trained with real data learned simpler representation. The authors ...
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Feb 18, 2017 · The title is Deep nets don't learn via memorization, and there is no learning without generalization. Upvote 2. Downvote Reply reply
It is established that there are qualitative differences when learning noise vs. natural datasets, and that for appropriately tuned explicit regularization, ...
Aug 2, 2024 · Bibliographic details on Deep Nets Don't Learn via Memorization.
Apr 6, 2017 · We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep ...
We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data in spite of overlyexpressive ...
Feb 22, 2017 · This paper (Deep Nets Don't Learn via Memorization – Felix Lau – Medium) provides a few empirical results to suggest that deep nets are capable.
Jul 23, 2019 · Memorization is the same as overfitting. The memory is implicitly represented by your weights. If your network does have enough parameters ...