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 2901–2925 of 10307 papers

TitleStatusHype
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning—0
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement—0
Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models—0
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Detecting Offensive Tweets in Hindi-English Code-Switched Language—0
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments—0
Detecting Privacy Requirements from User Stories with NLP Transfer Learning Models—0
Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work—0
Detecting Social Media Manipulation in Low-Resource Languages—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Better Transfer Learning with Inferred Successor Maps—0
Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models—0
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning—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
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation—0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography—0
Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN—0
``Deep'' Learning : Detecting Metaphoricity in Adjective-Noun Pairs—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