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Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
[article]
2020
arXiv
pre-print
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in cases when training data is scarce, when a single learner needs to perform multiple tasks, or when one wishes to encode priors in the network. The most commonly employed approaches for network adaptation are fine-tuning and using the pre-trained network as a fixed feature extractor, among others. In this paper, we
arXiv:1912.13503v4
fatcat:dkbrxaqvffh2vhlatzvvx2ygsu