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 6776–6800 of 10307 papers

TitleStatusHype
Translate and Classify: Improving Sequence Level Classification for English-Hindi Code-Mixed DataCode0
Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems—0
How transfer learning impacts linguistic knowledge in deep NLP models?—0
Adaptive Multi-Source Causal Inference—0
A study on the plasticity of neural networks—0
Bounded logit attention: Learning to explain image classifiersCode0
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language RepresentationCode0
Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts—0
Transfer Learning under High-dimensional Generalized Linear Models—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Risk-Aware Transfer in Reinforcement Learning using Successor Features—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning—0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
Investigating label suggestions for opinion mining in German Covid-19 social mediaCode0
Neural Network Training Using _1-Regularization and Bi-fidelity Data—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
Extremely low-resource machine translation for closely related languages—0
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching—0
Using Early-Learning Regularization to Classify Real-World Noisy Data—0
Towards Understanding Knowledge Distillation—0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI—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