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 2601–2650 of 6661 papers

TitleStatusHype
Guarding Barlow Twins Against Overfitting with Mixed SamplesCode1
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning—0
Long-Tail Learning with Rebalanced Contrastive Loss—0
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image CaptioningCode1
Just-in-Time Detection of Silent Security Patches—0
Hypergraph Contrastive Learning for Drug Trafficking Community DetectionCode1
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
Text Attribute Control via Closed-Loop Disentanglement—0
A Generalizable Deep Learning System for Cardiac MRICode0
Spectral Temporal Contrastive Learning—0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
Optimal Sample Complexity of Contrastive Learning—0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
Manipulating the Label Space for In-Context Classification—0
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation LearningCode1
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation—0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks—0
Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning AttacksCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation—0
SenTest: Evaluating Robustness of Sentence Encoders—0
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
SatCLIP: Global, General-Purpose Location Embeddings with Satellite ImageryCode2
BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
MultiCBR: Multi-view Contrastive Learning for Bundle RecommendationCode1
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data—0
Enhancing Item-level Bundle Representation for Bundle RecommendationCode0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight DetectionCode1
SCStory: Self-supervised and Continual Online Story DiscoveryCode0
Robust Basket Recommendation via Noise-tolerated Graph Contrastive LearningCode0
Text2Loc: 3D Point Cloud Localization from Natural LanguageCode0
BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights—0
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation—0
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance—0
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning—0
Class Gradient Projection For Continual LearningCode0
Incorporating granularity bias as the margin into contrastive loss for video captioning—0
Weakly-Supervised Audio-Visual Segmentation—0
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning—0
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses—0
Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment—0
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering—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