Self-Supervised Learning
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Benchmarks
DABS
STL-10
CIFAR-10
CIFAR-100
TinyImageNet
cifar10
cifar100
CREMA-D
Tiny ImageNet
Most implemented
A Simple Framework for Contrastive Learning of Visual Representations
Masked Autoencoders Are Scalable Vision Learners
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Emerging Properties in Self-Supervised Vision Transformers
Bootstrap your own latent: A new approach to self-supervised Learning
Supervised Contrastive Learning
Papers
Seven Sources of Physical AI Capability Formation
Capabilities relevant to Physical AI can arise from materially different formation histories, yet existing taxonomies organized by morphology, architecture, learning algorithm, task, or domain do not directly answer what…
Self-Supervised LearningAn Analysis of Self-supervised Pre-training with Dependent Samples
Self-supervised learning relies on so-called data augmentations $φ(x)$ of unlabeled datapoints $x$ --- for example, masking random pixels in an image $x$ --- that should leave the label of $x$ invariant and are often use…
Self-Supervised LearningLeveraging Imperfect Restoration for Data Availability Attack
The abundance of online data is at risk of unauthorized usage in training deep learning models. To counter this, various Data Availability Attacks (DAAs) have been devised to make data unlearnable for such models by subt…
Self-Supervised LearningSynergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology
In computational pathology (CPath), developing omni-modal self-supervised learning (SSL) models that integrate histology, genomics, and clinical reports enables transferable representation learning for whole slide images…
Self-Supervised LearningRepresentation LearningKnowledge Distillation During Mid-Training Favors Reasoning over Factual Recall
Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits are consistent across training stages remains unclear. Through contr…
Self-Supervised LearningKnowledge DistillationUncertainty of Vision Medical Foundation Models
Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches primarily rely on probability outputs from trained models (point predic…
Self-Supervised Learning