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 7301–7325 of 10307 papers

TitleStatusHype
AlexU-BackTranslation-TL at SemEval-2020 Task 12: Improving Offensive Language Detection Using Data Augmentation and Transfer Learning—0
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
RoBERT -- A Romanian BERT Model—0
Arabic Dialect Identification Using BERT Fine-TuningCode0
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning—0
Fine-grained domain classification using Transformers—0
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language DetectionCode0
Taxy.io@FinTOC-2020: Multilingual Document Structure Extraction using Transfer Learning—0
Task-Aware Representation of Sentences for Generic Text Classification—0
Bilingual Transfer Learning for Online Product Classification—0
Multilingual Neural Machine Translation—0
AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation—0
Claim extraction from text using transfer learning.—0
Improving Neural Machine Translation for Sanskrit-English—0
Context-Aware Text Normalisation for Historical Dialects—0
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource LanguagesCode0
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Unsupervised Representation Learning by Invariance Propagation—0
TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles—0
Transformer Models for Drug Adverse Effects Detection from Tweets—0
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches—0
Towards building a Robust Industry-scale Question Answering System—0
Towards the First Machine Translation System for Sumerian Transliterations—0
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values—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