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 2276–2300 of 10307 papers

TitleStatusHype
Deep Ensembles for Low-Data Transfer Learning—0
Breast mass detection in digital mammography based on anchor-free architecture—0
An Interpretable Knowledge Transfer Model for Knowledge Base Completion—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
Deep Ensembling for Perceptual Image Quality Assessment—0
Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning—0
Breast Cancer Image Classification Method Based on Deep Transfer Learning—0
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data—0
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising—0
DeepEthnic: Multi-Label Ethnic Classification from Face Images—0
Deepfake Detection of Face Images based on a Convolutional Neural Network—0
An Initial Investigation of Non-Native Spoken Question-Answering—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
BreakingNews: Article Annotation by Image and Text Processing—0
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization—0
A Concise Review of Transfer Learning—0
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
An information-Theoretic Approach to Semi-supervised Transfer Learning—0
Brain Tumor Detection Using Deep Learning Approaches—0
Tuned Inception V3 for Recognizing States of Cooking Ingredients—0
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
Deep Embedding Kernel—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