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 8851–8900 of 10307 papers

TitleStatusHype
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Cross-lingual Transfer Learning for Japanese Named Entity Recognition—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination—0
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain AdaptationCode0
Transfer Learning in Natural Language Processing—0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator NetworkCode0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Augmenting Transfer Learning with Semantic Reasoning—0
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar PhysicsCode0
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Style transfer-based image synthesis as an efficient regularization technique in deep learning—0
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network—0
Application of DenseNet in Camera Model Identification and Post-processing Detection—0
ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks—0
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Efficient Neural Task Adaptation by Maximum Entropy Initialization—0
Improved object recognition using neural networks trained to mimic the brain's statistical propertiesCode0
Using Deep Networks and Transfer Learning to Address Disinformation—0
A general method for regularizing tensor decomposition methods via pseudo-data—0
Brain-mediated Transfer Learning of Convolutional Neural Networks—0
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence MatchingCode0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Network Pruning via Transformable Architecture SearchCode0
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese—0
Zero-shot Knowledge Transfer via Adversarial Belief MatchingCode0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Semi-Supervised Learning with Scarce AnnotationsCode0
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation NetworkCode0
Pretraining for Conditional Generation with Pseudo Self Attention—0
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images—0
Adversarially robust transfer learningCode0
Activity Recognition and Prediction in Real Homes—0
Sparse Transfer Learning via Winning Lottery TicketsCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
Transfer Learning based Detection of Diabetic Retinopathy from Small DatasetCode0
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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