Self-Supervised Learning
self-supervised-learning-08e39466·3 events·first seen 21d agoAliases: Self-Supervised Learning
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Representation-Conditioned Diffusion Models for Controllable Image Generation
This paper explores conditioning diffusion models on representations from pre-trained self-supervised models as an alternative to text prompts or semantic maps, which require large annotated datasets. The self-conditioning mechanism improves unconditional image generation quality and provides a controllable representation space. The authors identify directions of variation in this space and demonstrate smoothness and disentanglement properties, suggesting potential for fine-grained generative control without heavy annotation overhead.
RayDer: Scalable Self-Supervised Novel View Synthesis via Unified Feed-Forward Transformer
RayDer is a unified feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone for self-supervised novel view synthesis (NVS). By treating dynamic content as a nuisance factor absorbed by a minimal dynamic state, it enables stable training on unconstrained real-world video without requiring dynamic-scene reconstruction. The model exhibits clean power-law scaling with both data and compute across multiple model sizes, and achieves zero-shot open-set performance competitive with supervised state-of-the-art methods on multiple benchmarks.
CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
CaMBRAIN is a Mamba-based causal state space model designed for real-time, continuous inference on variable-length EEG signals, addressing quadratic scaling limitations of attention-based models. It introduces a multi-stage self-supervised training pipeline for long-range memory retention and achieves state-of-the-art results across three EEG datasets with over 10x throughput improvement.