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 5501–5550 of 6661 papers

TitleStatusHype
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment—0
Learning to Predict Visual Attributes in the Wild—0
Learning to Summarize Videos by Contrasting Clips—0
Learning Tracking Representations from Single Point Annotations—0
Learning Transformation-Predictive Representations for Detection and Description of Local Features—0
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering—0
Learning Universal Sentence Embeddings with Large-scale Parallel Translation Datasets—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Learning Video Representations of Human Motion From Synthetic Data—0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
Learning Visual Composition through Improved Semantic Guidance—0
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method—0
Learn to Discover Dialog Intents via Self-supervised Context Pretraining—0
Learn What NOT to Learn: Towards Generative Safety in Chatbots—0
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Lesion Search with Self-supervised Learning—0
Less can be more in contrastive learning—0
Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures—0
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses—0
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Leveraging Contrastive Learning for Few-shot Geolocation of Social Posts—0
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances—0
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation—0
Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation—0
Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging LLMs for Multimodal Retrieval-Augmented Radiology Report Generation via Key Phrase Extraction—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases—0
Leveraging the Third Dimension in Contrastive Learning—0
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning—0
Lifelong Person Re-Identification with Backward-Compatibility—0
Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation—0
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning—0
Linking Representations with Multimodal Contrastive Learning—0
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection—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