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 5101–5150 of 6661 papers

TitleStatusHype
Semi-supervised Semantic Segmentation with Error Localization NetworkCode1
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition—0
Rethinking Visual Geo-localization for Large-Scale ApplicationsCode2
Lip to Speech Synthesis with Visual Context Attentional GANCode1
Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
POS-BERT: Point Cloud One-Stage BERT Pre-TrainingCode1
Bayesian Negative Sampling for RecommendationCode0
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment AnalysisCode0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks—0
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity RecognitionCode0
Transformer-Empowered Content-Aware Collaborative Filtering—0
Automatic Biomedical Term Clustering by Learning Fine-grained Term RepresentationsCode1
Graph Enhanced Contrastive Learning for Radiology Findings SummarizationCode1
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense EmbeddingsCode1
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation—0
Marginal Contrastive Correspondence for Guided Image Generation—0
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt TuningCode0
Video-Text Representation Learning via Differentiable Weak Temporal AlignmentCode1
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning—0
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval—0
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action LocalizationCode1
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCoCode1
Controllable Augmentations for Video Representation Learning—0
Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval—0
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended VersionCode0
Large-Scale Pre-training for Person Re-identification with Noisy LabelsCode2
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
Fair Contrastive Learning for Facial Attribute ClassificationCode1
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal—0
Contrasting the landscape of contrastive and non-contrastive learningCode0
SIOD: Single Instance Annotated Per Category Per Image for Object DetectionCode1
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning—0
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools SegmentationCode3
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene SegmentationCode1
Expanding Low-Density Latent Regions for Open-Set Object DetectionCode1
UNICON: Combating Label Noise Through Uniform Selection and Contrastive LearningCode1
Large-scale Bilingual Language-Image Contrastive LearningCode1
Frame-wise Action Representations for Long Videos via Sequence Contrastive LearningCode1
Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors SupervisionCode0
Mugs: A Multi-Granular Self-Supervised Learning FrameworkCode1
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive LearningCode1
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs with Language Structures via Dependency RelationshipsCode1
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking—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