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

Showing 776800 of 6661 papers

TitleStatusHype
ReContrast: Domain-Specific Anomaly Detection via Contrastive ReconstructionCode1
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-TrainingCode1
rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological MeasurementCode1
ContraBAR: Contrastive Bayes-Adaptive Deep RLCode1
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement LearningCode1
Supervised Adversarial Contrastive Learning for Emotion Recognition in ConversationsCode1
Self Contrastive Learning for Session-based RecommendationCode1
Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive LearningCode1
Training neural operators to preserve invariant measures of chaotic attractorsCode1
LIV: Language-Image Representations and Rewards for Robotic ControlCode1
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment AnalysisCode1
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural NetworksCode1
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-TuningCode1
Whitening-based Contrastive Learning of Sentence EmbeddingsCode1
Matrix Information Theory for Self-Supervised LearningCode1
Hierarchical Verbalizer for Few-Shot Hierarchical Text ClassificationCode1
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly DetectionCode1
RankCSE: Unsupervised Sentence Representations Learning via Learning to RankCode1
Towards Open-World Segmentation of PartsCode1
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based RecommendationCode1
Contrastive Learning of Sentence Embeddings from ScratchCode1
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent ClassificationCode1
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text ClusteringCode1
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes ChangeCode1
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified