This research introduces a domain generalization framework, MISA (Mutual Information-driven Separator with Spectral Alignment), which disentangles and learns semantic features via mutual information optimization and spectral alignment.
Abstract
With the continual advancement of computer vision techniques, the efficacy of deep learning models has markedly improved. Nonetheless, these accomplishments depend on the premise that both training and test data adhere to the independent and identically distributed (i.i.d.) property. This assumption is frequently violated in practice due to the variability of unobserved data distributions, resulting in performance deterioration. These variations are named domains, and this has motivated the study of robustness to domain shifts, commonly referred to as domain generalization (DG). In this context, DG methodologies have emerged as a novel research paradigm. This research introduces a domain generalization framework, MISA (Mutual Information-driven Separator with Spectral Alignment), which disentangles and learns semantic features via mutual information optimization and spectral alignment. MISA incorporates an attention mechanism and a domain classifier to extract domain information, while three complementary loss functions directly promote feature disentanglement. Additionally, we conceptually establish that the proposed components reduce generalization error in unseen domains and empirically verify the efficacy of MISA by comprehensive comparisons with existing methodologies.
This work proposes an innovative Environment-Invariant Subspace Learning (EISL) framework, which aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection and designs an Environmental Intervention module that generates diverse and challenging intervention pairs.
Shenghao Chen, Hao Jia, Chen Li et al.· 0 citations
We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy. We formalize these as the observation, prediction, and regularization principles and prove (i) that combining observation and prediction without regularization admits the constant encoder as a global minimizer under negative-free alignment; (ii) that the two objectives are gradient-complementary and structurally non-conflicting at the encoder output; and (iii) that the momentum encoder converges to the same fixed point as the online encoder and provides no collapse guarantee at convergence. Contrastive alignment provides only self-limiting collapse resistance, formalized via an explicit gradient-decay argument. Dropping prediction withholds the spatial training signal by construction; dropping observation forfeits cross-view semantic invariance by construction; at the scale we study, no pair substitutes for the third. Every major self-supervised method is a special case of a single unified energy decomposition. We pair every theoretical claim with a controlled experiment, including a patch-retrieval evaluation for the spatial consequence of prediction.
Efficient Unsupervised Domain Adaptation (EUDA) is proposed, a parameter-efficient framework that leverages a frozen DINOv2 backbone as a feature extractor and updates only a lightweight bottleneck and classification head to promote both discriminative learning and cross-domain alignment.
Ali Abedi, Q. M. J. Wu, Ning Zhang et al.· International Journal of Mac...· 9 citations
TASSO, a new paradigm that efficiently preserves the latent space geometry while ensuring network plasticity, is introduced with two complementary techniques: subspace learning and geometry-aware knowledge distillation.
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This paper studies what happens to the rest of the model when a class is forgotten, using a label-conditioned energy-based model (EBM) that assigns per-class energies, making the effect directly observable.
Syed Ali Ahmed, Syed Bilal Ahsan, Muhammad Zaigham Zaheer National University of Computer et al.· 0 citations
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