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Turning the weights to zero when training a neural network helps in reducingthe computational complexity at inference. To progressively increase thesparsity ratio in the network without causing sharp weight discontinuitiesduring training, our work combines soft-thresholding and straight-throughgradient estimation to update the raw, i.e. non-thresholded, version of zeroedweights. Our method, named ST-3 forstraight-through/soft-thresholding/sparse-training, obtains SoA results, bothin terms of accuracy/sparsity and accuracy/FLOPS trade-offs, when progressivelyincreasing the sparsity ratio in a single training cycle. In particular,despite its simplicity, ST-3 favorably compares to the most recent methods,adopting differentiable formulations or bio-inspired neuroregenerationprinciples. This suggests that the key ingredients for effective sparsificationprimarily lie in the ability to give the weights the freedom to evolve smoothlyacross the zero state while progressively increasing the sparsity ratio. Sourcecode and weights available at https://github.com/vanderschuea/stthree

Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?