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 4651–4675 of 6661 papers

TitleStatusHype
Improving Micro-video Recommendation by Controlling Position Bias—0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation—0
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Understanding Masked Image Modeling via Learning Occlusion Invariant Feature—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
OpenCon: Open-world Contrastive LearningCode1
Analyzing Data-Centric Properties for Graph Contrastive LearningCode0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange—0
XCon: Learning with Experts for Fine-grained Category DiscoveryCode1
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
SampleMatch: Drum Sample Retrieval by Musical Context—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
COCOA: Cross Modality Contrastive Learning for Sensor DataCode1
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation—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
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
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—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