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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 2701–2750 of 6661 papers

TitleStatusHype
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval—0
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images—0
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment—0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
Improve Unsupervised Pretraining for Few-label Transfer—0
BRIDO: Bringing Democratic Order to Abstractive Summarization—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)—0
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning—0
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Improving Continual Relation Extraction through Prototypical Contrastive Learning—0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation—0
Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity—0
Contrastive Learning with Negative Sampling Correction—0
Contrastive Learning with Nasty Noise—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Bridging the Emotional Semantic Gap via Multimodal Relevance Estimation—0
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps—0
Bridging Text and Image for Artist Style Transfer via Contrastive Learning—0
Anomalies, Representations, and Self-Supervision—0
Contrastive Learning with Counterfactual Explanations for Radiology Report Generation—0
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries—0
Annotation-Efficient Untrimmed Video Action Recognition—0
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
Contrastive Learning with Adversarial Examples—0
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
Contrastive Learning Via Equivariant Representation—0
Contrastive Learning Using Spectral Methods—0
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application—0
Graph Contrastive Learning under Heterophily via Graph Filters—0
An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding—0
Contrastive Learning to Improve Retrieval for Real-world Fact Checking—0
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Contrastive Learning Through Time—0
Contrastive Learning Subspace for Text Clustering—0
Bridge the Gap between Language models and Tabular Understanding—0
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification—0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study—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