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 4201–4250 of 6661 papers

TitleStatusHype
PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching—0
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion—0
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints—0
Pedestrian Detection by Exemplar-Guided Contrastive Learning—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
PepGB: Facilitating peptide drug discovery via graph neural networks—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning—0
PersonaBooth: Personalized Text-to-Motion Generation—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation—0
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns—0
Personalized Prompt for Sequential Recommendation—0
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations—0
Personalized Topic Selection Model for Topic-Grounded Dialogue—0
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification—0
PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained Human Action Recognition—0
PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching—0
Phase Matching for Out-of-Distribution Generalization—0
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis—0
Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment—0
Pic@Point: Cross-Modal Learning by Local and Global Point-Picture Correspondence—0
PIE: Physics-inspired Low-light Enhancement—0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering—0
PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting—0
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation—0
PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities—0
Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning—0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
PointCloud-Text Matching: Benchmark Datasets and a Baseline—0
Point Cloud Understanding via Attention-Driven Contrastive Learning—0
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
Polybot: Training One Policy Across Robots While Embracing Variability—0
Poly-View Contrastive Learning—0
Pose Invariant Topological Memory for Visual Navigation—0
Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning—0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
Positive-Negative Equal Contrastive Loss for Semantic Segmentation—0
Positive Unlabeled Contrastive Learning—0
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning—0
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples—0
Practical token pruning for foundation models in few-shot conversational virtual assistant systems—0
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation—0
PreDet: Large-Scale Weakly Supervised Pre-Training for Detection—0
Predicting conversion of mild cognitive impairment to Alzheimer's disease—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