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 3551–3600 of 6661 papers

TitleStatusHype
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Few-Shot Nested Named Entity Recognition—0
Few-shot Object Detection with Refined Contrastive Learning—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning—0
FewUser: Few-Shot Social User Geolocation via Contrastive Learning—0
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics—0
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge—0
FIESTA: Autoencoders for accurate fiber segmentation in tractography—0
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization—0
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning—0
Fine-Grained Classification with Noisy Labels—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Fine Grained Insider Risk Detection—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—0
Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning—0
Fine-grained Text to Image Synthesis—0
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning—0
Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents—0
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
FLAP: Fast Language-Audio Pre-training—0
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing—0
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation—0
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—0
FMP: Toward Fair Graph Message Passing against Topology Bias—0
f-Mutual Information Contrastive Learning—0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
Focus-Driven Contrastive Learning for Medical Question Summarization—0
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse—0
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation—0
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction—0
Forward-Forward Contrastive Learning—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—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