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Found 6 papers, 1 papers with code
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
Object Detection as a Positive-Unlabeled Problem
These missing annotations can be problematic, as the standard cross-entropy loss employed to train object detection models treats classification as a positive-negative (PN) problem: unlabeled regions are implicitly assumed to be background.
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts.
More Generalized and Personalized Unsupervised Representation Learning In A Distributed System
In this work, we propose a novel method, FedStyle, to learn a more generalized global model by infusing local style information with local content information for contrastive learning, and to learn more personalized local models by inducing local style information for downstream tasks.
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks.
Efficient Quantization Strategies for Latent Diffusion Models
By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies.
