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 2251–2275 of 10307 papers

TitleStatusHype
TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QACode1
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?Code1
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal DataCode0
Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure—0
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation—0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
Cross-lingual Transfer Learning for Javanese Dependency Parsing—0
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation ModelsCode1
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks—0
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
HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation DistillationCode1
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
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
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
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and ChallengesCode0
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing—0
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems—0
Using i-vectors for subject-independent cross-session EEG transfer learning—0
Surface-Enhanced Raman Spectroscopy and Transfer Learning Toward Accurate Reconstruction of the Surgical Zone—0
Transferring Core Knowledge via Learngenes—0
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense PredictionCode0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language 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