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 5901–5925 of 10307 papers

TitleStatusHype
Maximizing Audio Event Detection Model Performance on Small Datasets Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation Study—0
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model—0
Simple Control Baselines for Evaluating Transfer Learning—0
Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation—0
Source data selection for out-of-domain generalization—0
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction—0
A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model—0
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents—0
Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions—0
Detecting Privacy Requirements from User Stories with NLP Transfer Learning Models—0
Auto-Transfer: Learning to Route Transferrable RepresentationsCode0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Deep Reference Priors: What is the best way to pretrain a model?Code0
Investigating Transfer Learning in Graph Neural Networks—0
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition—0
Deep Learning MacroeconomicsCode0
Image-based eeg classification of brain responses to song recordingsCode0
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation—0
Transfer Learning for Estimation of Pendubot Angular Position Using Deep Neural Networks—0
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking—0
Transfer Learning In Differential Privacy's Hybrid-Model—0
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation—0
Cause-Effect Preservation and Classification using Neurochaos Learning—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