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 8501–8525 of 10307 papers

TitleStatusHype
Brain MRI Tumor Segmentation with Adversarial Networks—0
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis—0
Fine-grained Sentiment Classification using BERTCode0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy—0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Piracy Resistant Watermarks for Deep Neural NetworksCode0
Self-Supervised Representation Learning From Multi-Domain Data—0
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation—0
Attention Bridging Network for Knowledge Transfer—0
Multilingual End-to-End Speech Translation—0
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding—0
Few-Shot Image Recognition With Knowledge TransferCode0
Practical Deep Learning for Cloud, Mobile, and EdgeCode0
Deep learning for Chemometric and non-translational dataCode0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Generating Abstractive Summaries with Finetuned Language Models—0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Learning Robust Data Representation: A Knowledge Flow Perspective—0
On Generalizing Detection Models for Unconstrained EnvironmentsCode0
Generative One-Shot Face Recognition—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