MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation

Ronyu Zhang; Aosong Cheng; Gaole Dai; Yulin Luo; Jiaming Liu; Li Du; Huanrui Yang; Dan Wang; Leyuan Fang; Yuan Du; Shanghang Zhang

arXiv · 2605.17743

MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation

Ronyu Zhang·Aosong Cheng·Gaole Dai·Yulin Luo·Jiaming Liu·Li Du·Huanrui Yang·Dan Wang·Leyuan Fang·Yuan Du·Shanghang Zhang

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Abstract

Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture-biased backbones risk error accumulation and catastrophic forgetting. Drawing inspiration from the process of decoupling shape and texture in the human visual system, we introduce MoASE, a plug-in mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture using Activation Sparsity Experts with Spatial Differentiable Dropout, forming complementary high- and low-activation pathways, while high- and low-rank bottlenecks diversify representations. The Activation Sparsity Gate produces input-adaptive SDD thresholds for precise token selection, and the Domain-Aware Router assigns per-sample expert weights using texture-sensitive cues. To curb confirmation bias on unlabeled streams and stabilize supervision, we then introduce Domain-Adaptive On-Policy Distillation to constitute MoASE++, with an EMA-anchored on-policy reverse KL distillation and an augmentation policy conditioned on entropy and confidence that aligns predictions across the same views and improves the robustness-plasticity balance. Extensive experiments on classification (CIFAR-10/100-C, ImageNet-C) and semantic segmentation (Cityscapes->ACDC) demonstrate consistent state-of-the-art performance, offering a principled, controllable approach to continual adaptation in dynamic visual environments.

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Ronyu Zhang, Aosong Cheng, Gaole Dai, Yulin Luo, Jiaming Liu, Li Du, Huanrui Yang, Dan Wang, Leyuan Fang, Yuan Du, Shanghang Zhang. 2026-05-18. MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation. https://arxiv.org/abs/2605.17743

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