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 7501–7550 of 10307 papers

TitleStatusHype
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers—0
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—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
Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment—0
Pushing the Limits of AMR Parsing with Self-Learning—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Complete Multilingual Neural Machine Translation—0
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Optimising the Performance of Convolutional Neural Networks across Computing Systems using Transfer Learning—0
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
Technical Question Answering across Tasks and DomainsCode0
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification—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
Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216Code0
Inferring symmetry in natural languageCode0
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation—0
Deep Learning based Automated Forest Health Diagnosis from Aerial Images—0
SIGTYP 2020 Shared Task: Prediction of Typological Features—0
Multi-task Learning of Negation and Speculation for Targeted Sentiment ClassificationCode0
Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer LearningCode0
Explicit Alignment Objectives for Multilingual Bidirectional EncodersCode0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training—0
Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning ModelsCode0
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign DropoutCode0
Deep Ensembles for Low-Data Transfer Learning—0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora—0
Which Model to Transfer? Finding the Needle in the Growing Haystack—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Model Selection for Cross-Lingual TransferCode0
Multilingual Argument Mining: Datasets and Analysis—0
Land Cover Semantic Segmentation Using ResUNet—0
Asking Crowdworkers to Write Entailment Examples: The Best of Bad OptionsCode0
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence—0
Multi-Stage Pre-training for Low-Resource Domain Adaptation—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
fairseq S2T: Fast Speech-to-Text Modeling with fairseqCode0
Shape-aware Generative Adversarial Networks for Attribute Transfer—0
Multilingual Offensive Language Identification with Cross-lingual EmbeddingsCode0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—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