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
Quantum Transfer Learning for Acceptability Judgements—0
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered ChatbotCode1
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
Knowledge Distillation of Black-Box Large Language Models—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
PersianMind: A Cross-Lingual Persian-English Large Language Model—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation—0
Zero Resource Cross-Lingual Part Of Speech Tagging—0
Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis—0
VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern RecognitionCode0
Consensus Focus for Object Detection and minority classesCode0
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data—0
Neural Population Learning beyond Symmetric Zero-sum Games—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
Low-Resource Vision Challenges for Foundation Models—0
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need—0
Transfer-Learning-Based Autotuning Using Gaussian CopulaCode1
Anatomy of Neural Language ModelsCode0
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time SeriesCode4
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification—0
Logits Poisoning Attack in Federated Distillation—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Time-lapse seismic inversion for CO2 saturation with SeisCO2Net: An application to Frio-II siteCode1
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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