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 4851–4900 of 6661 papers

TitleStatusHype
TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics—0
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering—0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification—0
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
Task-Induced Representation Learning—0
Taxonomy Inference for Tabular Data Using Large Language Models—0
Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data—0
TCBERT: A Technical Report for Chinese Topic Classification BERT—0
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry—0
Temperature-Free Loss Function for Contrastive Learning—0
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Temporal Contrastive Learning for Spiking Neural Networks—0
Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models—0
Temporal Contrastive Learning with Curriculum—0
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Temporal Perceiving Video-Language Pre-training—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition—0
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder—0
Text Attribute Control via Closed-Loop Disentanglement—0
Text-aware and Context-aware Expressive Audiobook Speech Synthesis—0
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection—0
Text-Driven Tumor Synthesis—0
Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning—0
Text-to-Audio Generation Synchronized with Videos—0
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning—0
TextToon: Real-Time Text Toonify Head Avatar from Single Video—0
Text Transformations in Contrastive Self-Supervised Learning: A Review—0
Textual Entailment with Dynamic Contrastive Learning for Zero-shot NER—0
TG-VQA: Ternary Game of Video Question Answering—0
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation—0
The Championship-Winning Solution for the 5th CLVISION Challenge 2024—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data—0
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning—0
The Influences of Color and Shape Features in Visual Contrastive Learning—0
The JHU submission to VoxSRC-21: Track 3—0
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation—0
The Met Dataset: Instance-level Recognition for Artworks—0
The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning—0
The Others: Naturally Isolating Out-of-Distribution Samples for Robust Open-Set Semi-Supervised Learning—0
The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—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