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 7501–7525 of 10307 papers

TitleStatusHype
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering—0
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents—0
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate ControlCode0
Optimising the Performance of Convolutional Neural Networks across Computing Systems using Transfer Learning—0
Complete Multilingual Neural Machine Translation—0
Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment—0
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Pushing the Limits of AMR Parsing with Self-Learning—0
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification—0
Technical Question Answering across Tasks and DomainsCode0
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining—0
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning—0
Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks—0
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement—0
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation—0
Deep Learning based Automated Forest Health Diagnosis from Aerial Images—0
Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216Code0
Multi-task Learning of Negation and Speculation for Targeted Sentiment ClassificationCode0
Inferring symmetry in natural languageCode0
SIGTYP 2020 Shared Task: Prediction of Typological Features—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