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This paper proposes Intelligent Partitioning for Self-supervised Denoising (iPSD), a method enabling unsupervised training of deep EEG denoisers by partitioning noisy segments without requiring clean reference data.
This paper introduces NATD-GSSL, a framework evaluating the robustness of Graph Self-Supervised Learning on noisy, text-driven biomedical graphs. It demonstrates that certain GNN architectures and pretext tasks maintain performance despite real-world noise, offering practical guidance for unsupervised learning in imperfect datasets.
A researcher shares their struggle with achieving only ~50% accuracy using SSL methods (BYOL, MAE, VICReg) for hyperspectral crop stress classification on cabbage nitrogen deficiency detection, seeking advice on SSL techniques, feature engineering, and model architectures better suited for spectral data.
This paper proposes augmenting visual instruction tuning in multimodal language models with self-supervised tasks expressed as natural language instructions, improving vision-centric reasoning without additional architecture or annotations. By reformulating classical self-supervised pretext tasks as image-instruction-response triplets, the method achieves consistent performance improvements across multiple benchmarks by injecting only 3-10% visually grounded instructions into the training data.
TIPSv2 introduces enhanced vision-language pretraining techniques including patch-level distillation, an upgraded masked image objective (iBOT++), and improved caption sampling strategies to achieve superior dense patch-text alignment. The resulting family of image-text encoder models demonstrates strong performance across 9 tasks and 20 datasets.
Introduces Next-Latent Prediction (NextLat), a self-supervised objective that trains transformers to predict their next latent state, encouraging compact internal world models and improving generalization across sequence modeling tasks.
This paper introduces Self-Supervised Prompt Optimization (SPO), a framework that optimizes prompts for LLMs without external references by using output comparisons, significantly reducing costs and data requirements.