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 4701–4750 of 6661 papers

TitleStatusHype
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse—0
Simplified Transfer Learning for Chest Radiography Models Using Less DataCode1
FedX: Unsupervised Federated Learning with Cross Knowledge DistillationCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance—0
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI AcquisitionsCode1
Real-time End-to-End Video Text Spotter with Contrastive Representation LearningCode0
Action-based Contrastive Learning for Trajectory Prediction—0
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify—0
Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial PatchesCode1
FashionViL: Fashion-Focused Vision-and-Language Representation LearningCode1
Action-conditioned On-demand Motion GenerationCode0
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training—0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text RetrievalCode1
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Benchmarking Omni-Vision Representation through the Lens of Visual RealmsCode1
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology ImagesCode1
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional NetworksCode1
An Asymmetric Contrastive Loss for Handling Imbalanced DatasetsCode0
Contrastive Adapters for Foundation Model Group Robustness—0
Prototypical Contrast Adaptation for Domain Adaptive Semantic SegmentationCode0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated ContentCode0
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping—0
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion RecognitionCode1
Dual Contrastive Learning for Spatio-temporal Representation—0
Contrastive Deep SupervisionCode1
Contrastive Learning for Online Semi-Supervised General Continual LearningCode0
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning—0
RUSH: Robust Contrastive Learning via Randomized Smoothing—0
A Closer Look at Invariances in Self-supervised Pre-training for 3D VisionCode0
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's DiseaseCode0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation—0
Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio RepresentationCode1
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Few-Example Clustering via Contrastive Learning—0
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
DLME: Deep Local-flatness Manifold EmbeddingCode1
Supervised Contrastive Learning Approach for Contextual Ranking—0
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization—0
Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext TasksCode2
Network Binarization via Contrastive LearningCode1
Unsupervised Learning for Human Sensing Using Radio Signals—0
Integrating multi-label contrastive learning with dual adversarial graph neural networks for cross-modal retrievalCode1
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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