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 1301–1325 of 6661 papers

TitleStatusHype
Modeling Text-Label Alignment for Hierarchical Text ClassificationCode1
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation—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
Contrastive Learning with Synthetic PositivesCode1
Maven: A Multimodal Foundation Model for Supernova ScienceCode1
Enhancing Sound Source Localization via False Negative EliminationCode1
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched EmbeddingsCode0
EMP: Enhance Memory in Data Pruning—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
Online pre-training with long-form videos—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
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Learning Tree-Structured Composition of Data AugmentationCode0
Contrastive Learning Subspace for Text Clustering—0
SelEx: Self-Expertise in Fine-Grained Generalized Category DiscoveryCode1
Improving Nonlinear Projection Heads using Pretrained Autoencoder EmbeddingsCode0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning—0
Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled SamplesCode0
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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