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 2601–2625 of 10307 papers

TitleStatusHype
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning—0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning—0
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori—0
BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation—0
An Automated Deep Learning Approach for Bacterial Image Classification—0
Addressing Challenges in Data Quality and Model Generalization for Malaria Detection—0
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild—0
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets—0
An Audio-Based Fault Diagnosis Method for Quadrotors Using Convolutional Neural Network and Transfer Learning—0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering—0
Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Baselines for Reinforcement Learning in Text Games—0
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI—0
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
A bandit approach to curriculum generation for automatic speech recognition—0
Banana Sub-Family Classification and Quality Prediction using Computer Vision—0
Anomaly Detection in Images—0
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets—0
BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models—0
Detecting Social Media Manipulation in Low-Resource Languages—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