Regularisation: weight decay, dropout and augmentation · MAAI7602 Applied Computer Vision

Regularisation methods make a network rely less on the quirks of its training set: penalising large weights, dropping activations at random, or training on transformed images. Which changes to training improve generalisation when a network fits its training set but not new data?

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