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 10011025 of 6661 papers

TitleStatusHype
Graph Matching with Bi-level Noisy CorrespondenceCode1
Neural Machine Translation with Contrastive Translation MemoriesCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time SeriesCode1
Hyperbolic Contrastive Learning for Visual Representations beyond ObjectsCode1
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text PairsCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
Normalized Contrastive Learning for Text-Video RetrievalCode1
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image SegmentationCode1
Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head SynthesisCode1
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
Unsupervised Wildfire Change Detection based on Contrastive LearningCode1
SliceMatch: Geometry-guided Aggregation for Cross-View Pose EstimationCode1
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation RecognitionCode1
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing AugmentationsCode1
Pose-disentangled Contrastive Learning for Self-supervised Facial RepresentationCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Learning with Partial Labels from Semi-supervised PerspectiveCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive LearningCode1
Video Instance Shadow Detection Under the Sun and SkyCode1
Texts as Images in Prompt Tuning for Multi-Label Image RecognitionCode1
Transformer Based Multi-Grained Features for Unsupervised Person Re-IdentificationCode1
On Narrative Information and the Distillation of StoriesCode1
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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