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 3526–3550 of 10307 papers

TitleStatusHype
A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant—0
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph CompletionCode0
Named Entity Recognition Under Domain Shift via Metric Learning for Life SciencesCode0
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding—0
Transfer Learning in Human Activity Recognition: A Survey—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—0
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and ChallengesCode0
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense PredictionCode0
Transferring Core Knowledge via Learngenes—0
Surface-Enhanced Raman Spectroscopy and Transfer Learning Toward Accurate Reconstruction of the Surgical Zone—0
Using i-vectors for subject-independent cross-session EEG transfer learning—0
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems—0
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing—0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models—0
Quantum Transfer Learning for Acceptability Judgements—0
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
Knowledge Distillation of Black-Box Large Language 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
VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern RecognitionCode0
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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