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 4851–4900 of 6661 papers

TitleStatusHype
Words are all you need? Language as an approximation for human similarity judgments—0
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous DrivingCode1
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved NegativesCode1
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive DistillationCode1
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Semi-Supervised Learning for Mars Imagery Classification and Segmentation—0
From t-SNE to UMAP with contrastive learningCode1
Integrating Prior Knowledge in Contrastive Learning with KernelCode0
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group DiscriminationCode1
Egocentric Video-Language PretrainingCode2
3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation—0
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning—0
Hyperspherical Consistency RegularizationCode1
Prefix Conditioning Unifies Language and Label Supervision—0
Hard Negative Sampling Strategies for Contrastive Representation Learning—0
Cross-lingual and Multilingual CLIPCode2
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization—0
Positive Unlabeled Contrastive Learning—0
Multi-scale frequency separation network for image deblurring—0
Strongly Augmented Contrastive ClusteringCode1
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
Towards Generalisable Audio Representations for Audio-Visual Navigation—0
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks—0
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded ViewsCode0
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Generalized Supervised Contrastive Learning—0
Dog nose print matching with dual global descriptor based on Contrastive LearningCode1
3D Graph Contrastive Learning for Molecular Property Prediction—0
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark DiscoveryCode0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images—0
Contrasting quadratic assignments for set-based representation learningCode0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
CropMix: Sampling a Rich Input Distribution via Multi-Scale CroppingCode0
EMS: Efficient and Effective Massively Multilingual Sentence Embedding LearningCode0
Contrastive Representation Learning for 3D Protein Structures—0
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Self-Supervised Visual Representation Learning with Semantic GroupingCode1
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor EmbeddingCode0
CoNT: Contrastive Neural Text GenerationCode2
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning—0
FaIRCoP: Facial Image Retrieval using Contrastive Personalization—0
A Closer Look at Self-Supervised Lightweight Vision TransformersCode1
Multimodal Masked Autoencoders Learn Transferable RepresentationsCode1
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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