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 5076–5100 of 6661 papers

TitleStatusHype
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion RecognitionCode1
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment DetectionCode1
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity RecognitionCode1
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
A Token-level Contrastive Framework for Sign Language TranslationCode0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning—0
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels—0
Robust Cross-Modal Representation Learning with Progressive Self-Distillation—0
Self-Supervised Video Representation Learning with Motion-Contrastive Perception—0
Probabilistic Representations for Video Contrastive Learning—0
Contrastive language and vision learning of general fashion conceptsCode2
Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation LearningCode0
BankNote-Net: Open dataset for assistive universal currency recognitionCode1
Unified Contrastive Learning in Image-Text-Label SpaceCode2
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Audio-Visual Person-of-Interest DeepFake DetectionCode1
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency—0
RODD: A Self-Supervised Approach for Robust Out-of-Distribution DetectionCode1
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment RepresentationsCode1
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations—0
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive LearningCode0
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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