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The vast majority of deep models use multiple gradient signals, typicallycorresponding to a sum of multiple loss terms, to update a shared set oftrainable weights. However, these multiple updates can impede optimal trainingby pulling the model in conflicting directions. We present Gradient SignDropout (GradDrop), a probabilistic masking procedure which samples gradientsat an activation layer based on their level of consistency. GradDrop isimplemented as a simple deep layer that can be used in any deep net andsynergizes with other gradient balancing approaches. We show that GradDropoutperforms the state-of-the-art multiloss methods within traditional multitaskand transfer learning settings, and we discuss how GradDrop reveals linksbetween optimal multiloss training and gradient stochasticity.

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout