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 2576–2600 of 10307 papers

TitleStatusHype
Addressing materials' microstructure diversity using transfer learning—0
Bayesian Optimization of Bilevel Problems—0
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms—0
Detecting Cadastral Boundary from Satellite Images Using U-Net model—0
Detecting Social Media Manipulation in Low-Resource Languages—0
Detection of COVID19 in Chest X-Ray Images Using Transfer Learning—0
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables—0
Designing Category-Level Attributes for Discriminative Visual Recognition—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Bayesian Model Adaptation for Crowd Counts—0
An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic—0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI—0
Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother—0
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images—0
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
Dermoscopic Image Classification with Neural Style Transfer—0
Bayesian Experience Reuse for Learning from Multiple Demonstrators—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