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 6751–6775 of 10307 papers

TitleStatusHype
Multilingual Neural Semantic Parsing for Low-Resourced LanguagesCode0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
MexPub: Deep Transfer Learning for Metadata Extraction from German Publications—0
A Survey on Deep Domain Adaptation for LiDAR Perception—0
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities—0
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
Causality in Neural Networks -- An Extended Abstract—0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Multitask Learning for Emotionally Analyzing Sexual Abuse DisclosuresCode0
Parallel sentences mining with transfer learning in an unsupervised setting—0
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain—0
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word EmbeddingCode0
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN—0
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant DomainCode0
Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal EnsembleCode0
Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer LearningCode0
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization—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