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 2676–2700 of 6661 papers

TitleStatusHype
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—0
Intent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification—0
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection—0
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning—0
Scalable Deep Metric Learning on Attributed Graphs—0
KAAE: Numerical Reasoning for Knowledge Graphs via Knowledge-aware Attributes Learning—0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Conditional Distribution Learning on GraphsCode0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives—0
CLIC: Contrastive Learning Framework for Unsupervised Image Complexity RepresentationCode0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder—0
Collaborative Contrastive Network for Click-Through Rate Prediction—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
Relational Contrastive Learning and Masked Image Modeling for Scene Text RecognitionCode0
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT—0
Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image—0
Federated Contrastive Learning of Graph-Level Representations—0
EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare PredictionCode0
Learning Differentiable Surrogate Losses for Structured Prediction—0
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation—0
CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning—0
D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical ClassificationCode0
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data—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