Synergy-of-Experts: Collaborate to Improve Adversarial Robustness

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

Bibtex Paper Supplemental


Sen Cui, Jingfeng ZHANG, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang


Learning adversarially robust models require invariant predictions to a small neighborhood of its natural inputs, often encountering insufficient model capacity. There is research showing that learning multiple sub-models in an ensemble could mitigate this insufficiency, further improving the generalization and the robustness. However, the ensemble's voting-based strategy excludes the possibility that the true predictions remain with the minority. Therefore, this paper further improves the ensemble through a collaboration scheme---Synergy-of-Experts (SoE). Compared with the voting-based strategy, the SoE enables the possibility of correct predictions even if there exists a single correct sub-model. In SoE, every sub-model fits its specific vulnerability area and reserves the rest of the sub-models to fit other vulnerability areas, which effectively optimizes the utilization of the model capacity. Empirical experiments verify that SoE outperforms various ensemble methods against white-box and transfer-based adversarial attacks.