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 4576–4600 of 10307 papers

TitleStatusHype
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning—0
Neural Sign Language Translation by Learning Tokenization—0
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension—0
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units—0
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia—0
Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation—0
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
Neutral TTS Female Voice Corpus in Brazilian Portuguese—0
New Directions for Language Resource Development and Distribution—0
New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture—0
New Transfer Learning Techniques for Disparate Label Sets—0
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor—0
NITE: A Neural Inductive Teaching Framework for Domain Specific NER—0
NITK-IT_NLP@NSURL2019: Transfer Learning based POS Tagger for Under Resourced Bhojpuri and Magahi Language—0
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization—0
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis—0
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysis—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