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 4526–4550 of 10307 papers

TitleStatusHype
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
Guided Recommendation for Model Fine-Tuning—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
Habitat classification from satellite observations with sparse annotations—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hand Pose Classification Based on Neural Networks—0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
Hard instance learning for quantum adiabatic prime factorization—0
Harmless Transfer Learning for Item Embeddings—0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning—0
Hate-Speech and Offensive Language Detection in Roman Urdu—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—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