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 7076–7100 of 10307 papers

TitleStatusHype
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning—0
Concept drift-tolerant transfer learning in dynamic environments.—0
Customizing Contextualized Language Models forLegal Document Reviews—0
Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learningCode0
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning—0
Transfer Learning Approach for Arabic Offensive Language Detection System -- BERT-Based Model—0
Deep Multilabel CNN for Forensic Footwear Impression Descriptor Identification—0
Improving filling level classification with adversarial training—0
Overhead MNIST: A Benchmark Satellite Dataset—0
MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks—0
Rapid Classification of Glaucomatous Fundus Images—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Damage detection using in-domain and cross-domain transfer learning—0
A bandit approach to curriculum generation for automatic speech recognition—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection—0
Transfer Learning in Bandits with Latent Continuity—0
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification—0
Deep CNNs for large scale species classification—0
Detecting Bias in Transfer Learning Approaches for Text Classification—0
Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis—0
Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation—0
A Novel Transfer Learning-Based Approach for Screening Pre-existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds—0
Real-time detection of uncalibrated sensors using Neural Networks—0
Scaling Laws for Transfer—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