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 5426–5450 of 10307 papers

TitleStatusHype
KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition—0
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition—0
Efficient Entity Candidate Generation for Low-Resource LanguagesCode0
Neural Networks can Learn Representations with Gradient Descent—0
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition—0
GERNERMED++: Transfer Learning in German Medical NLPCode0
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique—0
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark—0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding—0
Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 DetectionCode0
A View Independent Classification Framework for Yoga Postures—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
Discovering Salient Neurons in Deep NLP Models—0
Transfer Learning via Test-Time Neural Networks Aggregation—0
Transfer learning for ensembles: reducing computation time and keeping the diversity—0
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
An Intensity and Phase Stacked Analysis of Phase-OTDR System using Deep Transfer Learning and Recurrent Neural Networks—0
Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI—0
Mutual Information-guided Knowledge Transfer for Novel Class Discovery—0
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World EnvironmentsCode0
Gated Domain Units for Multi-source Domain GeneralizationCode0
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms—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