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 4051–4100 of 6661 papers

TitleStatusHype
Self-Distilled Representation Learning for Time Series—0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding—0
Lesion Search with Self-supervised Learning—0
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training—0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Robust Contrastive Learning With Theory Guarantee—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person SearchCode0
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains—0
Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual SpeechCode0
Correlation-aware active learning for surgery video segmentation—0
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning—0
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual AlignmentCode0
Contrastive Learning for Multi-Object Tracking with TransformersCode0
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition—0
AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection—0
Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification—0
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image RetrievalCode0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Visual Commonsense based Heterogeneous Graph Contrastive Learning—0
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningCode0
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples—0
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation—0
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree TransformationCode0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-CollapseCode0
Towards a Unified Framework of Contrastive Learning for Disentangled Representations—0
Learning Discriminative Features for Crowd Counting—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
Sparse Contrastive Learning of Sentence Embeddings—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
CycleCL: Self-supervised Learning for Periodic Videos—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning—0
FLAP: Fast Language-Audio Pre-training—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identificationCode0
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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