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 1451–1500 of 6661 papers

TitleStatusHype
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting—0
Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks—0
Fusion Self-supervised Learning for Recommendation—0
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval—0
Boosting Graph Foundation Model from Structural Perspective—0
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local SimilaritiesCode2
ImagiNet: A Multi-Content Benchmark for Synthetic Image DetectionCode1
Hashing based Contrastive Learning for Virtual Screening—0
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number—0
Contextuality Helps Representation Learning for Generalized Category DiscoveryCode0
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention UnderstandingCode0
Domain Adaptive Lung Nodule Detection in X-ray Image—0
WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text SpottingCode0
MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-TrainingCode0
Start from Video-Music Retrieval: An Inter-Intra Modal Loss for Cross Modal Retrieval—0
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation—0
Multi-Modal CLIP-Informed Protein Editing—0
UniForensics: Face Forgery Detection via General Facial Representation—0
Contrastive Learning of Asset Embeddings from Financial Time SeriesCode2
Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based AdaptationCode1
DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes—0
Text-Region Matching for Multi-Label Image Recognition with Missing LabelsCode0
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models—0
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction—0
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
Banyan: Improved Representation Learning with Explicit Structure—0
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs—0
Intent-guided Heterogeneous Graph Contrastive Learning for RecommendationCode1
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Multi-label Cluster Discrimination for Visual Representation LearningCode4
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring—0
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation—0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Balanced Multi-Relational Graph ClusteringCode0
Distribution-Aware Robust Learning from Long-Tailed Data with Noisy LabelsCode0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
NV-Retriever: Improving text embedding models with effective hard-negative mining—0
Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance ModellingCode1
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QACode0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection—0
Self-supervised transformer-based pre-training method with General Plant Infection datasetCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentCode0
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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