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 3101–3150 of 6661 papers

TitleStatusHype
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity LinkingCode0
DWCL: Dual-Weighted Contrastive Learning for Multi-View ClusteringCode0
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive LearningCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial ImageryCode0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness EstimationCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Improving Query-by-Vocal Imitation with Contrastive Learning and Audio PretrainingCode0
Improving Sentence Similarity Estimation for Unsupervised Extractive SummarizationCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction FollowingCode0
Combined Scaling for Zero-shot Transfer Learning—0
Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
Dual Space Graph Contrastive Learning—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix—0
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
Dual-domain Collaborative Denoising for Social Recommendation—0
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction—0
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment—0
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Dual Contrastive Learning for Spatio-temporal Representation—0
Dual Contrastive Learning for General Face Forgery Detection—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
Collaborative Visual Place Recognition through Federated Learning—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation—0
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection—0
Collaborative Contrastive Network for Click-Through Rate Prediction—0
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing—0
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age—0
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
CoKe: Localized Contrastive Learning for Robust Keypoint Detection—0
A Unified and Efficient Contrastive Learning Framework for Text Summarization—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—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