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 1101–1125 of 10307 papers

TitleStatusHype
Collusion Detection with Graph Neural Networks—0
Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM VolumesCode0
Seg2Act: Global Context-aware Action Generation for Document Logical StructuringCode0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models—0
Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior—0
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery—0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning—0
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models—0
A Recurrent Neural Network Approach to the Answering Machine Detection Problem—0
Pre-Ictal Seizure Prediction Using Personalized Deep Learning—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Hyper-Representations: Learning from Populations of Neural NetworksCode1
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data—0
Learning De-Biased Representations for Remote-Sensing ImageryCode0
Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles—0
Interpolation-Free Deep Learning for Meteorological Downscaling on Unaligned Grids Across Multiple Domains with Application to Wind Power—0
Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models—0
Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning—0
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI—0
Universality in Transfer Learning for Linear Models—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—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