SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 2176–2200 of 6661 papers

TitleStatusHype
Trial and Error: Exploration-Based Trajectory Optimization for LLM AgentsCode3
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity—0
Partial Federated Learning—0
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional AdaptationCode0
SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos—0
Efficient Action Counting with Dynamic QueriesCode1
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment AnalysisCode0
Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment—0
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation—0
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction—0
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese—0
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media—0
Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning—0
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration—0
Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identificationCode2
Debiased Novel Category Discovering and Localization—0
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic TransferCode0
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models—0
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified