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 501–550 of 6661 papers

TitleStatusHype
A Universal Framework for Compressing Embeddings in CTR PredictionCode0
Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series ClassificationCode2
Hierarchical Context Transformer for Multi-level Semantic Scene UnderstandingCode0
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
Towards Efficient Contrastive PAC Learning—0
Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark DatasetCode0
ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution ErrorsCode0
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention HeadsCode0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language ModelsCode2
Contrastive Learning-Based privacy metrics in Tabular Synthetic DatasetsCode0
MVCNet: Multi-View Contrastive Network for Motor Imagery ClassificationCode1
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning FrameworkCode1
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code GenerationCode0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme DetectionCode1
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution GeneralizationCode0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views—0
Myna: Masking-Based Contrastive Learning of Musical RepresentationsCode1
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Time-series attribution maps with regularized contrastive learningCode5
Following the Autoregressive Nature of LLM Embeddings via Compression and AlignmentCode1
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for UAV-View Geo-LocalizationCode2
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference—0
CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic ScreeningCode0
Representation Learning on Out of Distribution in Tabular Data—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution SelectionCode0
MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage ClassificationCode1
SinSim: Sinkhorn-Regularized SimCLR—0
Graph Diffusion Network for Drug-Gene PredictionCode0
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Neuro-Symbolic Contrastive Learning for Cross-domain Inference—0
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation—0
A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters—0
Generalized Class Discovery in Instance Segmentation—0
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion—0
Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex InteractionsCode1
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI ClassificationCode1
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval—0
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell SegmentationCode0
Dataset Ownership Verification in Contrastive Pre-trained ModelsCode0
Supervised Contrastive Block Disentanglement—0
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
O1 Embedder: Let Retrievers Think Before Action—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