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 4251–4275 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
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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