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 8401–8425 of 10307 papers

TitleStatusHype
Feature discriminativity estimation in CNNs for transfer learning—0
Instance-based Transfer Learning for Multilingual Deep Retrieval—0
Cross-subject Decoding of Eye Movement Goals from Local Field Potentials—0
Extracting temporal features into a spatial domain using autoencoders for sperm video analysisCode0
Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging—0
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning—0
Transfer Learning in Spatial-Temporal Forecasting of the Solar Magnetic Field—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
Option Compatible Reward Inverse Reinforcement Learning—0
Model Adaption Object Detection System for Robot—0
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain MixingCode0
Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)Code0
Predictive modeling of brain tumor: A Deep learning approach—0
Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks—0
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse—0
Federated Adversarial Domain Adaptation—0
CloudifierNet -- Deep Vision Models for Artificial Image Processing—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning—0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Leveraging Medical Literature for Section Prediction in Electronic Health Records—0
BERT is Not an Interlingua and the Bias of TokenizationCode0
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems—0
Social IQa: Commonsense Reasoning about Social Interactions—0
PRADO: Projection Attention Networks for Document Classification On-Device—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