SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 5876–5900 of 10307 papers

TitleStatusHype
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition—0
Mixture of Online and Offline Experts for Non-stationary Time SeriesCode0
Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue ReportsCode0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning—0
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self AttentionCode0
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography—0
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation—0
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data—0
Consistency and Diversity induced Human Motion Segmentation—0
Development and Comparison of Scoring Functions in Curriculum Learning—0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
Deep Learning for Computational Cytology: A Survey—0
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression CaseCode0
Transferred Q-learning—0
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
Deep Feature Rotation for Multimodal Image Style Transfer—0
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural NetworksCode0
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Transferable Student Performance Modeling for Intelligent Tutoring Systems—0
Universal Spam Detection using Transfer Learning of BERT Model—0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified