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 4901–4950 of 6661 papers

TitleStatusHype
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses—0
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images—0
The Runner-up Solution for YouTube-VIS Long Video Challenge 2022—0
The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning—0
The Solution for Language-Enhanced Image New Category Discovery—0
The Solution for the CVPR2023 NICE Image Captioning Challenge—0
The token parser and manipulator, next-generation Deep Learning architecture—0
Semi-supervised Contrastive Learning Using Partial Label Information—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
Three Factors to Improve Out-of-Distribution Detection—0
Three Towers: Flexible Contrastive Learning with Pretrained Image Models—0
Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations—0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification—0
Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Time-Dependent VAE for Building Latent Representations from Visual Neural Activity with Complex Dynamics—0
Time-Equivariant Contrastive Video Representation Learning—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation—0
Time-Series Contrastive Learning against False Negatives and Class Imbalance—0
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport—0
Time to augment self-supervised visual representation learning—0
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
TMCIR: Token Merge Benefits Composed Image Retrieval—0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML—0
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval—0
Topic Modeling as Multi-Objective Contrastive Optimization—0
Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark—0
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation—0
TopoCL: Topological Contrastive Learning for Time Series—0
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders—0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
Towards an Interpretable Latent Space in Structured Models for Video Prediction—0
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning—0
Towards a Solution to Bongard Problems: A Causal Approach—0
Towards Attention-based Contrastive Learning for Audio Spoof Detection—0
Towards a Unified Framework of Contrastive Learning for Disentangled Representations—0
Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification—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