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 1251–1300 of 6661 papers

TitleStatusHype
Finetuning CLIP to Reason about Pairwise DifferencesCode1
Self-supervised Learning for Acoustic Few-Shot Classification—0
Turbo your multi-modal classification with contrastive learning—0
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image RetrievalCode4
Acoustic identification of individual animals with hierarchical contrastive learning—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding—0
Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data—0
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding—0
Multi-object event graph representation learning for Video Question Answering—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Multiplex Graph Contrastive Learning with Soft NegativesCode0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
A Unified Contrastive Loss for Self-TrainingCode0
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos—0
What to align in multimodal contrastive learning?—0
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval—0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and CorrespondencesCode0
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs—0
DetailCLIP: Detail-Oriented CLIP for Fine-Grained TasksCode2
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—0
PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching—0
Weakly-supervised Camera Localization by Ground-to-satellite Image RegistrationCode1
Contrastive Federated Learning with Tabular Data Silos—0
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysisCode2
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for RecommendationCode0
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
Dual-stream Feature Augmentation for Domain GeneralizationCode1
Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class PriorsCode0
Contrastive Learning-based User Identification with Limited Data on Smart Textiles—0
Dual-Level Cross-Modal Contrastive ClusteringCode0
Self-Supervised Contrastive Learning for Videos using Differentiable Local AlignmentCode0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
MOSMOS: Multi-organ segmentation facilitated by medical report supervision—0
SITAR: Semi-supervised Image Transformer for Action Recognition—0
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy ImagesCode0
Towards Generative Class Prompt Learning for Fine-grained Visual RecognitionCode0
EEG-Language Modeling for Pathology Detection—0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual 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