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 6401–6425 of 6661 papers

TitleStatusHype
Elsa: Energy-based learning for semi-supervised anomaly detection—0
Contrastive Domain Adaptation—0
Unsupervised Document Embedding via Contrastive Augmentation—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
Rethinking Deep Contrastive Learning with Embedding Memory—0
Jo-SRC: A Contrastive Approach for Combating Noisy Labels—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning—0
Bayesian Distributional Policy Gradients—0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders—0
Multi-Format Contrastive Learning of Audio Representations—0
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations—0
An Image-based Approach of Task-driven Driving Scene Categorization—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images—0
Bootstrapped Representation Learning on Graphs—0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
CoDeGAN: Contrastive Disentanglement for Generative Adversarial NetworkCode0
Fine-Grained Off-Road Semantic Segmentation and Mapping via 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