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Compressing the Backward Pass of Large-Scale Neural Architectures by Structured Activation Pruning
We report the effectiveness of activation pruning by evaluating training speed, accuracy, and memory usage of large-scale neural architectures on the example of ResMLP on image classification tasks.
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