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Pruning techniques have been successfully used in neural networks to tradeaccuracy for sparsity. However, the impact of network pruning is not uniform:prior work has shown that the recall for underrepresented classes in a datasetmay be more negatively affected. In this work, we study such relativedistortions in recall by hypothesizing an intensification effect that isinherent to the model. Namely, that pruning makes recall relatively worse for aclass with recall below accuracy and, conversely, that it makes recallrelatively better for a class with recall above accuracy. In addition, wepropose a new pruning algorithm aimed at attenuating such effect. Throughstatistical analysis, we have observed that intensification is less severe withour algorithm but nevertheless more pronounced with relatively more difficulttasks, less complex models, and higher pruning ratios. More surprisingly, weconversely observe a de-intensification effect with lower pruning ratios, whichindicates that moderate pruning may have a corrective effect to suchdistortions.

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm