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 41514200 of 6661 papers

TitleStatusHype
A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy ImagesCode0
SliceMatch: Geometry-guided Aggregation for Cross-View Pose EstimationCode1
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image SegmentationCode1
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix0
Unsupervised Wildfire Change Detection based on Contrastive LearningCode1
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
Towards Better Document-level Relation Extraction via Iterative InferenceCode0
Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head SynthesisCode1
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network0
Link Prediction with Non-Contrastive LearningCode0
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images0
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation RecognitionCode1
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene SegmentationCode0
Cross-domain Transfer of defect features in technical domains based on partial target data0
Pose-disentangled Contrastive Learning for Self-supervised Facial RepresentationCode1
Graph Contrastive Learning for Materials0
Learning with Partial Labels from Semi-supervised PerspectiveCode1
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs0
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing AugmentationsCode1
Few-shot Object Detection with Refined Contrastive Learning0
Self-supervised vision-language pretraining for Medical visual question answeringCode1
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?0
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation0
Texts as Images in Prompt Tuning for Multi-Label Image RecognitionCode1
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive LearningCode1
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations0
Video Instance Shadow Detection Under the Sun and SkyCode1
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition0
On Narrative Information and the Distillation of StoriesCode1
Transformer Based Multi-Grained Features for Unsupervised Person Re-IdentificationCode1
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction0
Unifying Vision-Language Representation Space with Single-tower Transformer0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier0
Expectation-Maximization Contrastive Learning for Compact Video-and-Language RepresentationsCode1
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing DeepfakesCode1
Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space ViewpointCode1
Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning0
Cross-Modal Contrastive Learning for Robust Reasoning in VQACode0
TCBERT: A Technical Report for Chinese Topic Classification BERT0
CLAWSAT: Towards Both Robust and Accurate Code ModelsCode0
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic GraphCode0
CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image FusionCode1
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method0
Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image TranslationCode1
Auto-Focus Contrastive Learning for Image Manipulation Detection0
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective0
Temporal Knowledge Graph Reasoning with Historical Contrastive LearningCode1
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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