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 5426–5450 of 10307 papers

TitleStatusHype
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond—0
Machine Learning Robustness: A Primer—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
Machine Learning Techniques for MRI Data Processing at Expanding Scale—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning—0
Machine Translation for a Very Low-Resource Language - Layer Freezing Approach on Transfer Learning—0
Machine Translation for Ge'ez Language—0
Machine Translation of Low-Resource Indo-European Languages—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR—0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0—0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
Magnituder Layers for Implicit Neural Representations in 3D—0
MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning—0
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages—0
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation—0
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
Making Person Search Enjoy the Merits of Person Re-identification—0
Malaria Cell Detection Using Deep Neural Networks—0
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework—0
Malware Classification Using Deep Boosted Learning—0
Malware Classification Using Transfer Learning—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