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 3001–3050 of 6661 papers

TitleStatusHype
EEG-Language Modeling for Pathology Detection—0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models—0
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation—0
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology—0
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
Online pre-training with long-form videos—0
EMP: Enhance Memory in Data Pruning—0
ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched EmbeddingsCode0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning—0
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
Retrieval Augmented Generation for Dynamic Graph Modeling—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Contrastive Learning Subspace for Text Clustering—0
Learning Tree-Structured Composition of Data AugmentationCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning—0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
Multimodal Contrastive In-Context Learning—0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning—0
QD-VMR: Query Debiasing with Contextual Understanding Enhancement for Video Moment Retrieval—0
Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled SamplesCode0
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model—0
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance—0
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections—0
Improving Query-by-Vocal Imitation with Contrastive Learning and Audio PretrainingCode0
Practical token pruning for foundation models in few-shot conversational virtual assistant systems—0
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding—0
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs—0
Estimated Audio-Caption Correspondences Improve Language-Based Audio RetrievalCode0
Universal Novelty Detection Through Adaptive Contrastive LearningCode0
Athena: Safe Autonomous Agents with Verbal Contrastive Learning—0
Breast tumor classification based on self-supervised contrastive learning from ultrasound videos—0
Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles—0
Just a Hint: Point-Supervised Camouflaged Object Detection—0
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation—0
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Uniting contrastive and generative learning for event sequences models—0
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning—0
Structure-enhanced Contrastive Learning for Graph Clustering—0
Resolving Lexical Bias in Edit Scoping with Projector Editor NetworksCode0
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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