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 8026–8050 of 10307 papers

TitleStatusHype
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection—0
Macsen: A Voice Assistant for Speakers of a Lesser Resourced LanguageCode0
Improving Sentence Boundary Detection for Spoken Language Transcripts—0
Can a powerful neural network be a teacher for a weaker neural network?Code0
Evaluating and Improving Child-Directed Automatic Speech Recognition—0
Pay Attention to Features, Transfer Learn Faster CNNs—0
Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline—0
SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost—0
Generative Adversarial Data Programming—0
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-RaysCode0
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minimaCode0
Learning to Ask Screening Questions for Job Postings—0
Investigating Transferability in Pretrained Language ModelsCode0
Deep Transfer Learning For Plant Center Localization—0
Deepfake Video Forensics based on Transfer Learning—0
Video Contents Understanding using Deep Neural Networks—0
Transfer Learning for Thermal Comfort Prediction in Multiple Cities—0
Meta-Learning for Few-Shot Land Cover Classification—0
Extending Multilingual BERT to Low-Resource Languages—0
Heterogeneous Representation Learning: A Review—0
A Cognition-Affect Integrated Model of Emotion—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—0
A scoping review of transfer learning research on medical image analysis using ImageNet—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