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 5651–5700 of 6661 papers

TitleStatusHype
Self-Supervised Learning Disentangled Group Representation as FeatureCode1
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Robust Contrastive Learning Using Negative Samples with Diminished SemanticsCode1
GenURL: A General Framework for Unsupervised Representation Learning—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
Deeper-GXX: Deepening Arbitrary GNNs—0
Towards More Generalizable One-shot Visual Imitation Learning—0
Contrastive Learning for Neural Topic ModelCode1
Image Quality Assessment using Contrastive LearningCode1
CLLD: Contrastive Learning with Label Distance for Text Classification—0
Contrastive Neural Processes for Self-Supervised LearningCode1
Multi-view Contrastive Graph ClusteringCode1
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIPCode1
No One Representation to Rule Them All: Overlapping Features of Training Methods—0
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval—0
Text-Based Person Search with Limited DataCode1
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Contrastive Document Representation Learning with Graph Attention Networks—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE—0
Understanding Dimensional Collapse in Contrastive Self-supervised LearningCode1
Contrastive Learning of Visual-Semantic Embeddings—0
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding—0
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning—0
Towards EEG signals codification using contrastiveloss—0
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word ProblemsCode1
Virtual Augmentation Supported Contrastive Learning of Sentence RepresentationsCode1
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Self-supervised Contrastive Attributed Graph Clustering—0
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling—0
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
OPEn: An Open-ended Physics Environment for Learning Without a Task—0
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase ExtractionCode1
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data—0
Decoupled Contrastive LearningCode1
Contrastive Learning Through Time—0
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health RecordsCode0
Weakly Supervised Contrastive LearningCode1
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation LearningCode1
Temperature as Uncertainty in Contrastive LearningCode1
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
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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