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 7576–7600 of 10307 papers

TitleStatusHype
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
Data-Efficient Pretraining via Contrastive Self-Supervision—0
Cross-Lingual Transfer Learning for Complex Word Identification—0
A Technical Question Answering System with Transfer LearningCode0
Attention-based Domain Adaption Using Transfer Learning for Part-of-Speech Tagging: An Experiment on the Hindi language—0
Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images—0
WeChat Neural Machine Translation Systems for WMT20—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Training general representations for remote sensing using in-domain knowledge—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
On Romanization for Model Transfer Between Scripts in Neural Machine Translation—0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
Improving Device Directedness Classification of Utterances with Semantic Lexical Features—0
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—0
Medical Image Segmentation Using Deep Learning: A SurveyCode0
Cross-Task Representation Learning for Anatomical Landmark Detection—0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Does Adversarial Transferability Indicate Knowledge Transferability?—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
Scalable Transfer Learning with Expert Models—0
Data Instance Prior for Transfer Learning in GANs—0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer—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