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 9501–9550 of 10307 papers

TitleStatusHype
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce—0
Crowd-Powered Data Mining—0
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech SynthesisCode0
Context-Aware Policy Reuse—0
Learning Multilingual Topics from Incomparable Corpus—0
Learning Answer Embeddings for Visual Question Answering—0
Semi-supervised and Transfer learning approaches for low resource sentiment classification—0
State Classification with CNN—0
Attention Based Fully Convolutional Network for Speech Emotion RecognitionCode0
Deep Image Compression via End-to-End LearningCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
TernausNetV2: Fully Convolutional Network for Instance SegmentationCode0
Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation—0
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems—0
Psychological State in Text: A Limitation of Sentiment Analysis—0
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation—0
Predicting Foreign Language Usage from English-Only Social Media Posts—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
SNU\_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning ComprehensionCode0
Domain Adaptation for MRI Organ Segmentation using Reverse Classification AccuracyCode0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Bootstrapping the Performance of Webly Supervised Semantic SegmentationCode0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Multi-Module Recurrent Neural Networks with Transfer Learning—0
Benchmarks and models for entity-oriented polarity detection—0
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension taskCode0
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System—0
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models—0
The Word Analogy Testing Caveat—0
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Multi-Label Transfer Learning for Multi-Relational Semantic Similarity—0
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition—0
Hyperspectral Imaging Technology and Transfer Learning Utilized in Identification Haploid Maize Seeds—0
Transductive Label Augmentation for Improved Deep Network Learning—0
Using transfer learning to detect galaxy mergers—0
Meta Transfer Learning for Facial Emotion Recognition—0
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy LabelsCode0
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis—0
Do Better ImageNet Models Transfer Better?—0
Meta-Learning for Low-Resource Neural Machine Translation—0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
Transfer Learning for Illustration ClassificationCode0
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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