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 6451–6500 of 6661 papers

TitleStatusHype
Learning to Anticipate Egocentric Actions by Imagination—0
Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning—0
Explicit homography estimation improves contrastive self-supervised learning—0
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification—0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation—0
Contrastive Learning for Recommender System—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
On Self-Supervised Image Representations for GAN Evaluation—0
Unsupervised Active Pre-Training for Reinforcement Learning—0
Towards Robust and Efficient Contrastive Textual Representation Learning—0
Unsupervised Word Alignment via Cross-Lingual Contrastive LearningCode0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Exploring Balanced Feature Spaces for Representation Learning—0
Self-supervised representation learning via adaptive hard-positive mining—0
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States—0
Pose Invariant Topological Memory for Visual Navigation—0
Contrastive Learning for Label Efficient Semantic Segmentation—0
A Flexible Framework for Discovering Novel Categories with Contrastive Learning—0
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-Training for Vision-Language RepresentationCode0
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML—0
Noise-Robust Contrastive Learning—0
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.Code0
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
Novelty Detection with Rotated Contrastive Predictive Coding—0
Self-supervised Temporal Learning—0
Auto-view contrastive learning for few-shot image recognition—0
Impact-driven Exploration with Contrastive Unsupervised Representations—0
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision—0
Towards Robust Textual Representations with Disentangled Contrastive Learning—0
Momentum Contrastive Autoencoder—0
Contrastive Video Textures—0
Fast Training of Contrastive Learning with Intermediate Contrastive Loss—0
PreDet: Large-Scale Weakly Supervised Pre-Training for Detection—0
Improving Generalizability of Protein Sequence Models via Data Augmentations—0
CLEAR: Contrastive Learning for Sentence Representation—0
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive LearningCode0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography—0
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's DiseaseCode0
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning—0
Adversarial Momentum-Contrastive Pre-TrainingCode0
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding—0
Motif-Driven Contrastive Learning of Graph Representations—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