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 9251–9275 of 10307 papers

TitleStatusHype
Cross-Lingual Argumentative Relation Identification: from English to PortugueseCode0
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Low-resource named entity recognition via multi-source projection: Not quite there yet?—0
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis—0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog—0
Gated Transfer Network for Transfer Learning—0
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains—0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
Accumulating Knowledge for Lifelong Online Learning—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Transfer of Deep Reactive Policies for MDP PlanningCode0
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning—0
Improving Document Binarization via Adversarial Noise-Texture AugmentationCode0
Universal Language Model Fine-Tuning with Subword Tokenization for PolishCode0
Training neural audio classifiers with few dataCode0
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Boosting pathology detection in infants by deep transfer learning from adult speech—0
How transferable are features in convolutional neural network acoustic models across languages?—0
Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks—0
Machine Learning Methods for Track Classification in the AT-TPCCode0
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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