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 251–300 of 6661 papers

TitleStatusHype
Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learningCode0
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReIDCode1
Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation—0
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion RecognitionCode1
MMHCL: Multi-Modal Hypergraph Contrastive Learning for RecommendationCode1
I-Con: A Unifying Framework for Representation Learning—0
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud DetectionCode3
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning—0
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
Improving Sound Source Localization with Joint Slot Attention on Image and Audio—0
Survey of Loss Augmented Knowledge Tracing—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
sEEG-based Encoding for Sentence Retrieval: A Contrastive Learning Approach to Brain-Language Alignment—0
SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training—0
Towards NSFW-Free Text-to-Image Generation via Safety-Constraint Direct Preference Optimization—0
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly DetectionCode0
Transformation of audio embeddings into interpretable, concept-based representations—0
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba DiffusionCode1
Consensus-aware Contrastive Learning for Group Recommendation—0
Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image AnalysisCode1
Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration—0
PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning—0
The Others: Naturally Isolating Out-of-Distribution Samples for Robust Open-Set Semi-Supervised Learning—0
CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training FrameworkCode0
Machine Learning Methods for Gene Regulatory Network Inference—0
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic DecompositionCode0
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data—0
PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond—0
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic ImagesCode0
TMCIR: Token Merge Benefits Composed Image Retrieval—0
Subset-Contrastive Multi-Omics Network Embedding—0
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution—0
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?—0
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space—0
STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data—0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification—0
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative FilteringCode1
On the Value of Cross-Modal Misalignment in Multimodal Representation LearningCode0
Integrating Textual Embeddings from Contrastive Learning with Generative Recommender for Enhanced PersonalizationCode1
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding—0
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation—0
Causal integration of chemical structures improves representations of microscopy images for morphological profilingCode0
Federated Prototype Graph Learning—0
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training—0
CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental LearningCode0
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive LearningCode1
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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