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 851–875 of 10307 papers

TitleStatusHype
SQ-Whisper: Speaker-Querying based Whisper Model for Target-Speaker ASRCode0
Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEsCode1
Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)—0
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation SystemsCode0
Assessing and Learning Alignment of Unimodal Vision and Language Models—0
Representation Purification for End-to-End Speech Translation—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
Streaming Detection of Queried Event StartCode0
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images—0
Memory-efficient Continual Learning with Neural Collapse Contrastive—0
A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic Net—0
SiTSE: Sinhala Text Simplification Dataset and EvaluationCode0
Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning—0
Transfer Learning for Control Systems via Neural Simulation Relations—0
Command-line Risk Classification using Transformer-based Neural Architectures—0
FathomVerse: A community science dataset for ocean animal discovery—0
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative ModelsCode1
The Evolution and Future Perspectives of Artificial Intelligence Generated Content—0
Patent-publication pairs for the detection of knowledge transfer from research to industry: reducing ambiguities with word embeddings and references—0
Pairwise Discernment of AffectNet Expressions with ArcFace—0
Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps—0
Pruned Convolutional Attention Network Based Wideband Spectrum Sensing with Sub-Nyquist SamplingCode0
Polish Medical Exams: A new dataset for cross-lingual medical knowledge transfer assessment—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