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Sep 26, 2019 · Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations. This paper ...
Oct 9, 2019 · Comment: This paper studies the transfer of representations learned by deep neural networks across various datasets and tasks when the network is pre-trained on ...
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Transferability is basically the desirable phenomenon that deep feature representations learned from one dataset can benefit optimization and generalization on ...
This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability, ...
Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations. This paper tries to ...
Abstract. Currently it is well known that deep neural networks are vulnerable to adversarial examples, constructed by applying small but malicious ...
Transferable adversarial training: A general approach to adapting deep classifiers ... Towards Understanding the Transferability of Deep Representations. H Liu, M ...
Abstract. Currently it is well known that deep neural networks are vulnerable to adversarial examples, constructed by applying small but malicious perturbations ...
Missing: Representations. | Show results with:Representations.
Mar 20, 2024 · Transferability: Deep representations learned from one task or domain often exhibit transferability, meaning they can be reused or fine-tuned ...
Oct 22, 2021 · Towards understanding the transferability of deep representations. ... Towards understanding the transferability of deep representations. arXiv ...