Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

ICLR 2020 Jeffrey O ZhangAlexander SaxAmir ZamirLeonidas GuibasJitendra Malik

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... (read more)

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