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 9501–9525 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
Psychological State in Text: A Limitation of Sentiment Analysis—0
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems—0
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
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
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension taskCode0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
Domain Adaptation for MRI Organ Segmentation using Reverse Classification AccuracyCode0
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
Benchmarks and models for entity-oriented polarity detection—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—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