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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3626–3650 of 6661 papers

TitleStatusHype
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection—0
Adapting to Observation Length of Trajectory Prediction via Contrastive Learning—0
Adaptive Data Augmentation for Contrastive Learning—0
Adaptive Dataset Quantization—0
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity—0
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification—0
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation—0
Adaptive Multi-User Channel Estimation Based on Contrastive Feature Learning—0
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation—0
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number—0
Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning—0
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity—0
A Deep Behavior Path Matching Network for Click-Through Rate Prediction—0
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore—0
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—0
A dual contrastive framework—0
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning—0
Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques—0
Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning—0
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
Adversarial Contrastive Estimation—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Show:102550
← PrevPage 146 of 267Next →
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