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 7301–7350 of 10307 papers

TitleStatusHype
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource LanguagesCode0
Improving Neural Machine Translation for Sanskrit-English—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning—0
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models—0
Bilingual Transfer Learning for Online Product Classification—0
Task-Aware Representation of Sentences for Generic Text Classification—0
Offensive Language Detection on Video Live Streaming Chat—0
Claim extraction from text using transfer learning.—0
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer LearningCode0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language DetectionCode0
Effective Few-Shot Classification with 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
Federated Learning for Spoken Language Understanding—0
Label Representations in Modeling Classification as Text Generation—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
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches—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
Unsupervised Representation Learning by Invariance Propagation—0
Towards building a Robust Industry-scale Question Answering System—0
TransMIA: Membership Inference Attacks Using Transfer Shadow Training—0
Self-Supervised Real-to-Sim Scene Generation—0
DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning—0
An Improved Transfer Model: Randomized Transferable Machine—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
Multi-objective Neural Architecture Search with Almost No Training—0
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectorsCode0
Autonomous learning of multiple, context-dependent tasks—0
Data-Efficient Classification of Radio GalaxiesCode0
Early Life Cycle Software Defect Prediction. Why? How?Code0
Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input—0
SSDL: Self-Supervised Domain Learning for Improved Face Recognition—0
Unsupervised Word Translation Pairing using Refinement based Point Set Registration—0
Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches—0
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio—0
Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review—0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Grafit: Learning fine-grained image representations with coarse labels—0
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network—0
Experiments on transfer learning architectures for biomedical relation extraction—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
Separating and denoising seismic signals with dual-path recurrent neural network architecture—0
Discovering Hidden Physics Behind Transport Dynamics—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
mForms : Multimodal Form-Filling with Question Answering—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