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 6201–6225 of 6661 papers

TitleStatusHype
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing—0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Unpaired Deep Image Deraining Using Dual Contrastive Learning—0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Self-supervised Product Quantization for Deep Unsupervised Image RetrievalCode0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition—0
The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description—0
Semantics-Guided Contrastive Network for Zero-Shot Object detection—0
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis—0
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-SupervisionCode0
Revisiting 3D ResNets for Video RecognitionCode0
Self-supervised Representation Learning for Trip Recommendation—0
Motifs-based Recommender System via Hypergraph Convolution and Contrastive LearningCode0
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning—0
Injecting Text in Self-Supervised Speech Pretraining—0
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging—0
Learning Cross-modal Contrastive Features for Video Domain Adaptation—0
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning—0
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning—0
Graph Contrastive Pre-training for Effective Theorem Reasoning—0
Support-Set Based Cross-Supervision for Video Grounding—0
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment—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