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 2301–2325 of 6661 papers

TitleStatusHype
BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning—0
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously—0
Constrained Multiview Representation for Self-supervised Contrastive Learning—0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
TimeSiam: A Pre-Training Framework for Siamese Time-Series ModelingCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition—0
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach—0
Self-Supervised Contrastive Learning for Long-term ForecastingCode2
Prototypical Contrastive Learning through Alignment and Uniformity for RecommendationCode1
Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive LossesCode0
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning—0
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive LearningCode1
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
Code Representation Learning At Scale—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties—0
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen—0
A Survey on Self-Supervised Learning for Non-Sequential Tabular DataCode3
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
Root Cause Analysis In Microservice Using Neural Granger Causal DiscoveryCode1
Instance Paradigm Contrastive Learning for Domain Generalization—0
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records—0
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be BetterCode0
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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