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 6201–6250 of 6661 papers

TitleStatusHype
Rethinking Deep Contrastive Learning with Embedding Memory—0
A Broad Study on the Transferability of Visual Representations with Contrastive LearningCode1
Supporting Clustering with Contrastive LearningCode1
Knowledge-aware Contrastive Molecular Graph Learning—0
Jo-SRC: A Contrastive Approach for Combating Noisy Labels—0
VLGrammar: Grounded Grammar Induction of Vision and LanguageCode1
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
COMPLETER: Incomplete Multi-view Clustering via Contrastive PredictionCode1
Bayesian Distributional Policy Gradients—0
Self-Supervised Classification NetworkCode1
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
SPICE: Semantic Pseudo-labeling for Image ClusteringCode1
Single Underwater Image Restoration by Contrastive LearningCode1
Training GANs with Stronger Augmentations via Contrastive DiscriminatorCode1
Contrastive Learning of Musical RepresentationsCode1
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image SegmentationCode1
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics ModelCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
Reconsidering Representation Alignment for Multi-view ClusteringCode1
3D Human Pose, Shape and Texture from Low-Resolution Images and VideosCode1
Multi-Format Contrastive Learning of Audio Representations—0
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders—0
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-TrainingCode1
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations—0
An Image-based Approach of Task-driven Driving Scene Categorization—0
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial ExamplesCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Spatially Consistent Representation LearningCode1
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Doubly Contrastive Deep ClusteringCode1
SimTriplet: Simple Triplet Representation Learning with a Single GPUCode1
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images—0
Bootstrapped Representation Learning on Graphs—0
CoDeGAN: Contrastive Disentanglement for Generative Adversarial NetworkCode0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
Self-Supervised Longitudinal Neighbourhood EmbeddingCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
Extending Contrastive Learning to Unsupervised Coreset Selection—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Deep Clustering by Semantic Contrastive Learning—0
Task-Adaptive Neural Network Search with Meta-Contrastive LearningCode1
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound ClassificationCode1
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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