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 2151–2200 of 6661 papers

TitleStatusHype
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation DistillationCode1
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
Inference via Interpolation: Contrastive Representations Provably Enable Planning and InferenceCode1
Intent-aware Recommendation via Disentangled Graph Contrastive Learning—0
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Contrastive Pre-training for Deep Session Data Understanding—0
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source LocalizationCode1
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives—0
Multi-Scale Subgraph Contrastive Learning—0
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples—0
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning—0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral ImagesCode0
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