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 5301–5325 of 10307 papers

TitleStatusHype
Grapes disease detection using transfer learning—0
Teacher Guided Training: An Efficient Framework for Knowledge Transfer—0
A Theory for Knowledge Transfer in Continual Learning—0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao—0
Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray ImagesCode0
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
The Moral Foundations Reddit CorpusCode0
Localizing the conceptual difference of two scenes using deep learning for house keeping usages—0
Comparison of semi-supervised learning methods for High Content Screening quality control—0
How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art ClassificationCode0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Continual Prune-and-Select: Class-incremental learning with specialized subnetworksCode0
Object Detection Using Sim2Real Domain Randomization for Robotic Applications—0
SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANsCode0
Continual Learning for Tumor Classification in Histopathology Images—0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add Data—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