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 7326–7350 of 10307 papers

TitleStatusHype
TransMIA: Membership Inference Attacks Using Transfer Shadow Training—0
DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning—0
Self-Supervised Real-to-Sim Scene Generation—0
Autonomous learning of multiple, context-dependent tasks—0
An Improved Transfer Model: Randomized Transferable Machine—0
Multi-objective Neural Architecture Search with Almost No Training—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectorsCode0
Early Life Cycle Software Defect Prediction. Why? How?Code0
Data-Efficient Classification of Radio GalaxiesCode0
Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input—0
SSDL: Self-Supervised Domain Learning for Improved Face Recognition—0
Unsupervised Word Translation Pairing using Refinement based Point Set Registration—0
Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches—0
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio—0
Grafit: Learning fine-grained image representations with coarse labels—0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review—0
Discovering Hidden Physics Behind Transport Dynamics—0
Separating and denoising seismic signals with dual-path recurrent neural network architecture—0
Experiments on transfer learning architectures for biomedical relation extraction—0
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
mForms : Multimodal Form-Filling with Question Answering—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