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 5426–5450 of 6661 papers

TitleStatusHype
Distributed Contrastive Learning for Medical Image Segmentation—0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange—0
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
Analyzing Data-Centric Properties for Graph Contrastive LearningCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
SampleMatch: Drum Sample Retrieval by Musical Context—0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering—0
Curriculum Learning for Data-Efficient Vision-Language Alignment—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning—0
Time to augment self-supervised visual representation learning—0
Optimizing transformations for contrastive learning in a differentiable framework—0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Contrastive Learning for Interactive Recommendation in Fashion—0
Online Continual Learning with Contrastive Vision Transformer—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
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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