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 9526–9550 of 10307 papers

TitleStatusHype
SNU\_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning ComprehensionCode0
Multi-Module Recurrent Neural Networks with Transfer Learning—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Bootstrapping the Performance of Webly Supervised Semantic SegmentationCode0
The Word Analogy Testing Caveat—0
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models—0
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Multi-Label Transfer Learning for Multi-Relational Semantic Similarity—0
Hyperspectral Imaging Technology and Transfer Learning Utilized in Identification Haploid Maize Seeds—0
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition—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
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Do Better ImageNet Models Transfer Better?—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