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 4301–4350 of 6661 papers

TitleStatusHype
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Contrastive Learning of Preferences with a Contextual InfoNCE Loss—0
Contrastive Learning of Sentence Representations—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition—0
Contrastive Learning of Visual-Semantic Embeddings—0
Contrastive Learning on Medical Intents for Sequential Prescription Recommendation—0
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study—0
Contrastive Learning Subspace for Text Clustering—0
Contrastive Learning Through Time—0
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex—0
Contrastive Learning to Improve Retrieval for Real-world Fact Checking—0
Graph Contrastive Learning under Heterophily via Graph Filters—0
Contrastive Learning Using Spectral Methods—0
Contrastive Learning Via Equivariant Representation—0
Contrastive Learning with Adversarial Examples—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
Contrastive Learning with Counterfactual Explanations for Radiology Report Generation—0
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps—0
Contrastive Learning with Nasty Noise—0
Contrastive Learning with Negative Sampling Correction—0
Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval—0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Contrastive Mean-Shift Learning for Generalized Category Discovery—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Contrastive Multi-Task Dense Prediction—0
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction—0
Contrastive Multi-view Framework for Customer Lifetime Value Prediction—0
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms—0
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive Pre-training for Deep Session Data Understanding—0
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs—0
Contrastive Pre-training for Zero-Shot Information Retrieval—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Contrastive Prompt Learning-based Code Search based on Interaction Matrix—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—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