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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 4551–4600 of 6661 papers

TitleStatusHype
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning—0
Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image ScenesCode0
Learning Music Sequence Representation from Text Supervision—0
Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs—0
Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition—0
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization—0
Self-supervised Vision Transformers for 3D Pose Estimation of Novel ObjectsCode0
Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-trainingCode0
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation—0
Event-Centric Query Expansion in Web Search—0
Controlled Text Generation with Hidden Representation TransformationsCode0
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining—0
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition—0
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER—0
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Three Towers: Flexible Contrastive Learning with Pretrained Image Models—0
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning—0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest NeighborsCode0
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance LearningCode0
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
TabGSL: Graph Structure Learning for Tabular Data Prediction—0
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression—0
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization—0
Contrastive Training of Complex-Valued Autoencoders for Object DiscoveryCode0
Clinically Labeled Contrastive Learning for OCT Biomarker Classification—0
Improving Factuality of Abstractive Summarization without Sacrificing Summary QualityCode0
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification—0
Optimizing Non-Autoregressive Transformers with Contrastive Learning—0
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine TranslationCode0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding—0
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning—0
Temporal Contrastive Learning for Spiking Neural Networks—0
Federated Generalized Category Discovery—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
Sentence Representations via Gaussian EmbeddingCode0
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment—0
Towards Unsupervised Recognition of Token-level Semantic Differences in Related DocumentsCode0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Transfer-Free Data-Efficient Multilingual Slot Labeling—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