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 3651–3700 of 6661 papers

TitleStatusHype
Graph Contrastive Learning with Cross-view Reconstruction—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
Adversarial Robustness of Link Sign Prediction in Signed Graphs—0
Adversarial Masking Contrastive Learning for vein recognition—0
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
Adversarial Training with Contrastive Learning in NLP—0
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing—0
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation—0
A Flexible Framework for Discovering Novel Categories with Contrastive Learning—0
A Framework for Generative and Contrastive Learning of Audio Representations—0
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
A Framework using Contrastive Learning for Classification with Noisy Labels—0
A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization—0
Generalized Supervised Contrastive Learning—0
A General Purpose Supervisory Signal for Embodied Agents—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
A Generic Method for Fine-grained Category Discovery in Natural Language Texts—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
Liquidity takers behavior representation through a contrastive learning approach—0
Age Prediction From Face Images Via Contrastive Learning—0
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization—0
AGPNet -- Autonomous Grading Policy Network—0
A Hybrid Approach for Document Layout Analysis in Document images—0
A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining—0
A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-Shot Representation Forecasting—0
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection—0
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
AI Foundation Models in Remote Sensing: A Survey—0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification—0
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation—0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision—0
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing—0
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering—0
Align Representations With Base: A New Approach to Self-Supervised Learning—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
All Beings Are Equal in Open Set Recognition—0
All-day Depth Completion via Thermal-LiDAR Fusion—0
Revisiting Catastrophic Forgetting in Class Incremental Learning—0
Alleviating the Sparsity of Open Knowledge Graphs with Pretrained Contrastive Learning—0
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—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