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 6001–6025 of 6661 papers

TitleStatusHype
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
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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