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- W4226185596 abstract "Batch Normalization is an essential component of all state-of-the-art neural networks architectures. However, since it introduces many practical issues, much recent research has been devoted to designing normalization-free architectures. In this paper, we show that weights initialization is key to train ResNet-like normalization-free networks. In particular, we propose a slight modification to the summation operation of a block output to the skip-connection branch, so that the whole network is correctly initialized. We show that this modified architecture achieves competitive results on CIFAR-10, CIFAR-100 and ImageNet without further regularization nor algorithmic modifications." @default.
- W4226185596 created "2022-05-05" @default.
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- W4226185596 date "2021-12-22" @default.
- W4226185596 modified "2023-09-27" @default.
- W4226185596 title "A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization" @default.
- W4226185596 doi "https://doi.org/10.48550/arxiv.2112.12299" @default.
- W4226185596 hasPublicationYear "2021" @default.
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