ExPLoRe: Expert Patch-Level Loss Routing for Multi-Objective Masked Image Modeling
For self-supervised vision researchers, ExPLoRe provides a principled way to handle spatial heterogeneity in multi-objective learning, with moderate gains over strong baselines.
ExPLoRe introduces per-patch loss coefficients via Soft MoE dispatch weights for multi-objective MIM, achieving +0.5% k-NN and +4.4% linear probe on ImageNet-1K with ViT-Base, and closing a 2.5-2.9 mIoU segmentation gap on ADE20K.
Multi-objective masked image modeling (MIM) combines complementary learning signals (token distillation, CLS alignment, and pixel reconstruction) but existing methods weight these objectives with global scalars, ignoring spatial heterogeneity across patches. We present ExPLoRe (Expert Patch-Level Loss Routing), which repurposes Soft Mixture of Experts (MoE) dispatch weights as learned, per-patch loss coefficients. The key mechanism is loss-coupling: allowing loss gradients to flow through dispatch weights to the router enables content-dependent specialization, where different patches receive different emphases across objectives. A detach ablation confirms loss-coupling as the core mechanism, degrading performance by 1.6% when gradients are blocked. On ImageNet-1K with ViT-Base, ExPLoRe improves over non-MoE baselines on two objective combinations (Token+CLS: +0.5% k-NN, +4.4% linear probe; Token+Pixel: +2.2% k-NN), achieving 80.6% linear probe and 85.3% finetuning accuracy, competitive with published methods. For downstream transfer, we develop adaptation recipes (Freeze Routing, Expert Dropout, and Freeze Attention) that improve MoE finetuning by +1.5% over the vanilla MoE, and close a 2.5--2.9 mIoU segmentation gap so that MoE models match or exceed non-MoE baselines on ADE20K.