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To replace data augmentation, this paper proposed a method called SLAP tointensify experience to speed up machine learning and reduce the sample size.SLAP is a model-independent protocol/function to produce the same output givendifferent transformation variants. SLAP improved the convergence speed ofconvolutional neural network learning by 83% in the experiments with Gomokugame states, with only one eighth of the sample size compared with dataaugmentation. In reinforcement learning for Gomoku, using AlphaGoZero/AlphaZero algorithm with data augmentation as baseline, SLAP reduced thenumber of training samples by a factor of 8 and achieved similar winning rateagainst the same evaluator, but it was not yet evident that it could speed upreinforcement learning. The benefits should at least apply to domains that areinvariant to symmetry or certain transformations. As future work, SLAP may aidmore explainable learning and transfer learning for domains that are notinvariant to symmetry, as a small step towards artificial general intelligence.

Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN  Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku  Reinforcement Learning