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 5901–5950 of 6661 papers

TitleStatusHype
Contrastive Learning for Unsupervised Video Highlight Detection—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein LocalizationCode0
Sparse and Complete Latent Organization for Geospatial Semantic Segmentation—0
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer—0
Representation Learning via Consistent Assignment of Views to ClustersCode0
Contrastive Learning of Semantic and Visual Representations for Text TrackingCode0
Frequency-Aware Contrastive Learning for Neural Machine Translation—0
Dual Contrastive Learning for General Face Forgery Detection—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Towards Universal GAN Image Detection—0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID—0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
Supervised Graph Contrastive Pretraining for Text Classification—0
AGPNet -- Autonomous Grading Policy Network—0
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification—0
Cross-modal Contrastive Learning for Speech Translation—0
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction—0
Contrastive Learning for Fair Representations—0
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification—0
CODER: An efficient framework for improving retrieval through COntextual Document Embedding RerankingCode0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Long Context Question Answering via Supervised Contrastive LearningCode0
Bayesian Graph Contrastive Learning—0
Knowledge-Rich Self-Supervision for Biomedical Entity Linking—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
Transferrable Contrastive Learning for Visual Domain Adaptation—0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes—0
Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness—0
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry—0
A Self-supervised Mixed-curvature Graph Neural Network—0
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study—0
Dual Cluster Contrastive learning for Object Re-IdentificationCode0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Contextualized Spatio-Temporal Contrastive Learning with Self-SupervisionCode0
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework—0
Boosting Contrastive Learning with Relation Knowledge Distillation—0
Contrastive Instruction-Trajectory Learning for Vision-Language NavigationCode0
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization—0
Time-Equivariant Contrastive Video Representation Learning—0
Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation LearningCode0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
Seeing Objects in dark with Continual Contrastive Learning—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
U2-Former: A Nested U-shaped Transformer for Image Restoration—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—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