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 3476–3500 of 10307 papers

TitleStatusHype
Enabling Continual Learning in Neural Networks with Meta Learning—0
Automatic Discovery of Novel Intents & Domains from Text Utterances—0
Exploiting Convolution Filter Patterns for Transfer Learning—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
Enabling hand gesture customization on wrist-worn devices—0
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning—0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Deep Embedding Kernel—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
Adaptive Variants of Optimal Feedback Policies—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification—0
End-to-end acoustic modelling for phone recognition of young readers—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings—0
End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries—0
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—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