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Coded ResNeXt: a network for designing disentangled information paths
[article]
2022
To avoid treating neural networks as highly complex black boxes, the deep learning research community has tried to build interpretable models allowing humans to understand the decisions taken by the model. Unfortunately, the focus is mostly on manipulating only the very high-level features associated with the last layers. In this work, we look at neural network architectures for classification in a more general way and introduce an algorithm which defines before the training the paths of the
doi:10.48550/arxiv.2202.05343
fatcat:36yrz7wrx5cgfovp6atzs2x76m