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 4401–4425 of 10307 papers

TitleStatusHype
Multilingual Transfer Learning for Children Automatic Speech Recognition—0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Multilingual Transfer Learning for QA Using Translation as Data Augmentation—0
Multilingual Universal Sentence Encoder for Semantic Retrieval—0
Multi-lingual Wikipedia Summarization and Title Generation On Low Resource Corpus—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—0
Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral—0
Multimodal Alignment and Fusion: A Survey—0
Multimodal and self-supervised representation learning for automatic gesture recognition in surgical robotics—0
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification—0
Multimodal Bearing Fault Classification Under Variable Conditions: A 1D CNN with Transfer Learning—0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Multi-modal cascade feature transfer for polymer property prediction—0
Multi-modal Deep Analysis for Multimedia—0
Multi-modal Deep Learning—0
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest—0
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples—0
Multi-Modal Depth Estimation Using Convolutional Neural Networks—0
Multi-Modal Emotion Detection with Transfer Learning—0
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—0
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models—0
Multimodal Knowledge Learning for Named Entity Disambiguation—0
Multimodal Quantitative Language for Generative Recommendation—0
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer 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