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 1801–1825 of 6661 papers

TitleStatusHype
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis—0
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual RepresentationsCode0
Rehearsal-free Federated Domain-incremental Learning—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—0
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language ModelsCode0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous DataCode0
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data—0
C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis—0
Efficient Model-Stealing Attacks Against Inductive Graph Neural NetworksCode0
Modeling User Fatigue for Sequential RecommendationCode1
CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph AugmentationCode1
DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical AlignmentCode2
Towards Graph Contrastive Learning: A Survey and Beyond—0
Transcriptomics-guided Slide Representation Learning in Computational PathologyCode2
Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification—0
Unveiling Key Aspects of Fine-Tuning in Sentence Embeddings: A Representation Rank Analysis—0
SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot DetectionCode1
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
INDUS: Effective and Efficient Language Models for Scientific Applications—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