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 5226–5250 of 6661 papers

TitleStatusHype
Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive LossCode0
Non-Contrastive Learning Meets Language-Image Pre-TrainingCode0
Correlation between Alignment-Uniformity and Performance of Dense Contrastive RepresentationsCode0
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent DiscoveryCode0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images—0
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph TrainingCode0
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog—0
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold—0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ SegmentationCode0
Attention-Based Audio Embeddings for Query-by-ExampleCode0
Indoor Smartphone SLAM with Learned Echoic Location Features—0
Improving Radiology Summarization with Radiograph and Anatomy Prompts—0
Instance Segmentation with Cross-Modal Consistency—0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
RaP: Redundancy-aware Video-language Pre-training for Text-Video RetrievalCode0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Closed-book Question Generation via Contrastive LearningCode0
Invariance-adapted decomposition and Lasso-type contrastive learning—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and WhistlesCode0
Language Agnostic Multilingual Information Retrieval with Contrastive LearningCode0
Pre-Training Representations of Binary Code Using Contrastive Learning—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