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 4251–4300 of 10307 papers

TitleStatusHype
Energy Predictive Models with Limited Data using Transfer Learning—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Energy efficient distributed analytics at the edge of the network for IoT environments—0
Energy Decay Network (EDeN)—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach—0
Energy Clustering for Unsupervised Person Re-identification—0
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training—0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition—0
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning—0
Chimpanzee voice prints? Insights from transfer learning experiments from human voices—0
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Active Adversarial Domain Adaptation—0
End-to-End Speech Translation of Arabic to English Broadcast News—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries—0
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
A Physics-preserved Transfer Learning Method for Differential Equations—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware—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 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
Encoding Explanatory Knowledge for Zero-shot Science Question Answering—0
Meta-models for transfer learning in source localisation—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training—0
Characterizing and Avoiding Negative Transfer—0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
Enabling Incremental Knowledge Transfer for Object Detection at the Edge—0
Enabling hand gesture customization on wrist-worn devices—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—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