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 7776–7800 of 10307 papers

TitleStatusHype
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization—0
Transformations between deep neural networks—0
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer—0
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures—0
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayErCode0
Robust Learning with Frequency Domain Regularization—0
The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain—0
robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots—0
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model—0
Deep Learning for Apple Diseases: Classification and Identification—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Transfer Learning for Electricity Price ForecastingCode0
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
On the application of transfer learning in prognostics and health management—0
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC TasksCode0
Learn Faster and Forget Slower via Fast and Stable Task Adaptation—0
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization—0
Hybrid deep learning architecture for general disruption prediction across tokamaks—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Low Resource Sequence Tagging using Sentence Reconstruction—0
Multi-Label and Multilingual News Framing Analysis—0
Multi-Cell Compositional LSTM for NER Domain Adaptation—0
Multi-Action Dialog Policy Learning with Interactive Human Teaching—0
Multi-Task Supervised Pretraining for Neural Domain Adaptation—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