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 3176–3200 of 10307 papers

TitleStatusHype
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning RepresentationsCode0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak—0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization—0
Zambezi Voice: A Multilingual Speech Corpus for Zambian LanguagesCode1
Towards End-to-end Speech-to-text SummarizationCode0
Masked Autoencoders are Efficient Continual Federated LearnersCode0
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera—0
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image SegmentationCode0
"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering improves Machine Translation—0
Subgraph Networks Based Contrastive Learning—0
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff—0
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot LearningCode3
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from CrowdsCode1
Input-gradient space particle inference for neural network ensemblesCode0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model—0
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge GraphsCode0
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image SegmentationCode1
Commonsense Knowledge Transfer for Pre-trained Language Models—0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning—0
TIES-Merging: Resolving Interference When Merging ModelsCode2
Transfer learning for atomistic simulations using GNNs and kernel mean embeddingsCode1
Context selectivity with dynamic availability enables lifelong continual learningCode0
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23—0
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy—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