Title: Does MIM cheat? Exploring Semantic Invariance in Self-Supervised ViT Representations
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Self-Supervised Learning, Representation Learning, Computer Vision

Abstract: In this work, we analyze patch-level embeddings and show that MIM objectives bias representations toward non-semantic cues, limiting their effectiveness in inference. To probe this effect, we introduce a model-agnostic counterfactual score that quantifies 
*semantic invariance* by comparing principal component responses to real inputs and noise, providing a novel measure to directly characterize the tradeoff between semantic information and structural noise in ViT embeddings. Building on this measure, we propose Semantic Orthogonal Projection (SOaP), a post-hoc method using simple Gram–Schmidt orthogonalization that suppresses invariant components in patch representations. Our experiments show that SOaP consistently improves performance on multiple downstream tasks across state-of-the-art MIM-based models.

TL;DR: We analyze embeddings of state-of-the-art ViTs to uncover semantically invariant features for all MIM-based models, and propose a new model agnostic projection for post-hoc denoising of representations.