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 7651–7675 of 10307 papers

TitleStatusHype
Transfer Learning under High-dimensional Generalized Linear Models—0
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification—0
Transfer Learning Under High-Dimensional Network Convolutional Regression Model—0
Transfer Learning using CNN for Handwritten Devanagari Character Recognition—0
Transfer Learning Using Logistic Regression in Credit Scoring—0
Transfer Learning using Neural Ordinary Differential Equations—0
Transfer Learning using Representation Learning in Massive Open Online Courses—0
Using Early-Learning Regularization to Classify Real-World Noisy Data—0
The SLT-Interactions Parsing System at the CoNLL 2018 Shared Task—0
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022—0
The Specificity and Helpfulness of Peer-to-Peer Feedback in Higher Education—0
Text-based automatic personality prediction: A bibliographic review—0
The UCF Systems for the LoResMT 2021 Machine Translation Shared Task—0
The Universitat d'Alacant Submissions to the English-to-Kazakh News Translation Task at WMT 2019—0
The University of Helsinki Submission to the IWSLT2020 Offline SpeechTranslation Task—0
The University of Maryland's Kazakh-English Neural Machine Translation System at WMT19—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
The Wisdom of a Crowd of Brains: A Universal Brain Encoder—0
The Word Analogy Testing Caveat—0
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy—0
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation—0
This joke is [MASK]: Recognizing Humor and Offense with Prompting—0
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning—0
Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models—0
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots—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