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 2726–2750 of 10307 papers

TitleStatusHype
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation—0
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time—0
Disposable Transfer Learning for Selective Source Task Unlearning—0
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs—0
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks—0
Deep Multi-Instance Transfer Learning—0
Deep Multilabel CNN for Forensic Footwear Impression Descriptor Identification—0
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States—0
Deep Neural Network-Based Sign Language Recognition: A Comprehensive Approach Using Transfer Learning with Explainability—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Deep Neural Network Models Compression—0
Deep neural network models for computational histopathology: A survey—0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Distilling Localization for Self-Supervised Representation Learning—0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text—0
Diversified Mutual Learning for Deep Metric Learning—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions—0
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Disentangled State Space Representations—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