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 1026–1050 of 6661 papers

TitleStatusHype
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
PARDON: Privacy-Aware and Robust Federated Domain GeneralizationCode0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring—0
A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization—0
Emotion-Guided Image to Music Generation—0
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification—0
Robust Variational Contrastive Learning for Partially View-unaligned ClusteringCode1
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge—0
Breccia and basalt classification of thin sections of Apollo rocks with deep learningCode0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
Beyond Positive History: Re-ranking with List-level Hybrid Feedback—0
DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive LearningCode2
Accelerating Augmentation Invariance Pretraining—0
PaPaGei: Open Foundation Models for Optical Physiological SignalsCode2
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—0
Idempotent Unsupervised Representation Learning for Skeleton-Based Action RecognitionCode0
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts—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