with Pretrained Generative Model for Self-Supervised Learning

Enhancing self-supervised representation learning through lightweight learnable data augmentation

Data augmentation plays a central role in self-supervised learning, as the quality and diversity of augmented views strongly influence the learned representations. However, most existing self-supervised methods rely on fixed stochastic augmentation pipelines, while more adaptive alternatives often require expensive policy search, adversarial training, or additional optimization procedures. In this paper, we propose a lightweight learnable augmentation framework based on Extreme Learning Machines (ELM) for self-supervised visual representation learning. The proposed module predicts image-dependent transformation parameters and applies them through a differentiable augmentation operator, enabling joint optimization with the representation model while introducing minimal additional computational overhead. The framework is integrated into three representative self-supervised learning methods: SimCLR, BYOL, and SimSiam. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet show that the proposed method consistently improves linear evaluation performance relative to reproduced baselines across most settings. In particular, the method yields notable gains on CIFAR datasets and remains effective on the more challenging Tiny ImageNet benchmark. A per-class difficulty analysis further shows that the proposed augmentation strategy substantially improves performance on hard classes, indicating stronger robustness to challenging categories while maintaining competitive overall performance. In general, the results demonstrate that lightweight learnable augmentation can effectively enhance self-supervised representation learning across different frameworks and datasets.

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distribution with augmented or generated samples, and recent text-to-image (T2I) diffusion models provide a strong generative prior for this purpose. However, diversity alone is insufficient for robust generalization, because useful generated samples should also capture variations that the current classifier finds difficult. Motivated by distributionally robust optimization (DRO), we define a semantic ambiguity set in the class-conditional generative space of a pretrained T2I model and search it for samples with high classification loss under the current classifier. To this end, we introduce PAPT++, a risk-aware adversarial generation-training framework for SDG. PAPT++ first learns diverse semantic reference images for each class through image-text alignment and intra-class diversity regularization. These references then serve as denoising targets during classifier-guided diffusion synthesis, reducing semantic drift while guiding generation toward challenging variations. The generated samples are combined with the source data to update the classifier, and the updated classifier guides the next synthesis round in return. In this way, PAPT++ progressively exposes the classifier to challenging yet semantically consistent variations. Extensive experiments on standard SDG benchmarks demonstrate the superiority of the proposed PAPT++ method and the effectiveness of its main components.

Self-Supervised Representation-Guided Generative Dataset Distillation

Self-supervised representation-guided generative dataset distillation (SRG) is proposed, a framework that translates the SSL geometry into diffusion guidance and consistently outperforms the evaluated generative baselines across multiple datasets and IPC settings.

ZeroDiff++: Generative Test-Time Adaptation for Zero-shot Learning.

Zero-shot Learning (ZSL) enables classifiers to recognize classes unseen during training, commonly via generative two stage methods: (1) learn visual semantic correlations from seen classes; (2) synthesize unseen class features from semantics to train classifiers. In this paper, we identify spurious visual semantic correlations in existing generative ZSL worsened by scarce seen class samples and introduce two metrics to quantify spuriousness for seen and unseen classes. Furthermore, we point out a more critical bottleneck: existing unadaptive fully noised generators produce features disconnected from real test samples, which also leads to the spurious correlation. To enhance the visual-semantic correlations on both seen and unseen classes, we propose ZeroDiff++, a diffusion-based generative framework. In training, ZeroDiff++ uses (i) diffusion augmentation to produce diverse noised samples, (ii) supervised contrastive (SC) representations for instance level semantics, and (iii) multi-view discriminators with Wasserstein mutual learning to assess generated features. At generation time, we introduce (iv) Diffusion-based Test time Adaptation (DiffTTA) to adapt the generator using pseudo label reconstruction, and (v) Diffusion-based Test time Generation (DiffGen) to trace the diffusion denoising path and produce partially synthesized features that connect real and generated data, and mitigates data scarcity further. Extensive experiments on three ZSL benchmarks demonstrate that ZeroDiff++ not only achieves significant improvements over existing ZSL methods but also maintains robust performance even with scarce training data.

Self-distillation framework via historical collaborative learning for image classification

Progressive Multi-Objective Optimization for Improved t-SNE Embeddings

A progressive multi-objective optimization framework is proposed that enhances t-SNE by integrating complementary loss functions, including a ranking-aware divergence (KLmax) and a Wasserstein-based term for global alignment.