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 3251–3300 of 6661 papers

TitleStatusHype
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Local Manifold Learning for No-Reference Image Quality Assessment—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive LearningCode0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
TopoGCL: Topological Graph Contrastive LearningCode0
Data curation via joint example selection further accelerates multimodal learning—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation LearningCode0
Enhancing OOD Detection Using Latent DiffusionCode0
The Championship-Winning Solution for the 5th CLVISION Challenge 2024—0
Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Speech Analysis of Language Varieties in ItalyCode0
Fine-grained Background Representation for Weakly Supervised Semantic SegmentationCode0
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss—0
A Contrastive Learning Approach to Mitigate Bias in Speech ModelsCode0
LARP: Language Audio Relational Pre-training for Cold-Start Playlist ContinuationCode0
Revealing Vision-Language Integration in the Brain with Multimodal NetworksCode0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Towards a multimodal framework for remote sensing image change retrieval and captioningCode0
Composite Concept Extraction through Backdooring—0
A Generic Method for Fine-grained Category Discovery in Natural Language Texts—0
Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models—0
RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning ProcessesCode0
COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport—0
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation—0
GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation—0
Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization—0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
Effective Generation of Feasible Solutions for Integer Programming via Guided DiffusionCode0
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach—0
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain TranslationCode0
InternalInspector I^2: Robust Confidence Estimation in LLMs through Internal States—0
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMsCode0
Balancing Embedding Spectrum for RecommendationCode0
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding—0
On the Effectiveness of Supervision in Asymmetric Non-Contrastive LearningCode0
SemanticMIM: Marring Masked Image Modeling with Semantics Compression for General Visual RepresentationCode0
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval—0
Shelf-Supervised Cross-Modal Pre-Training for 3D Object DetectionCode0
Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual SimilarityCode0
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning—0
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation—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