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 4851–4875 of 6661 papers

TitleStatusHype
CTRLStruct: Dialogue Structure Learning for Open-Domain Response GenerationCode0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning—0
On the Provable Advantage of Unsupervised Pretraining—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation—0
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant PhenotypingCode0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
PE-GAN: Prior Embedding GAN for PXD images at Belle IICode0
Edge computing on TPU for brain implant signal analysisCode0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
The Trade-off between Universality and Label Efficiency of Representations from Contrastive LearningCode0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for RecommendationCode0
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation—0
A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion—0
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations—0
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition—0
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering—0
DCLP: Neural Architecture Predictor with Curriculum Contrastive LearningCode0
Introducing Depth into Transformer-based 3D Object Detection—0
Flexible Phase Dynamics for Bio-Plausible Contrastive LearningCode0
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images—0
Improving Sentence Similarity Estimation for Unsupervised Extractive SummarizationCode0
Amortised Invariance Learning for Contrastive Self-SupervisionCode0
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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