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 3001–3025 of 6661 papers

TitleStatusHype
EEG-Language Modeling for Pathology Detection—0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models—0
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology—0
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation—0
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
Online pre-training with long-form videos—0
EMP: Enhance Memory in Data Pruning—0
ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched EmbeddingsCode0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search—0
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
Retrieval Augmented Generation for Dynamic Graph Modeling—0
Contrastive Learning Subspace for Text Clustering—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Learning Tree-Structured Composition of Data AugmentationCode0
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning—0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
QD-VMR: Query Debiasing with Contextual Understanding Enhancement for Video Moment Retrieval—0
Multimodal Contrastive In-Context Learning—0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot 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