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 9076–9100 of 10307 papers

TitleStatusHype
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN—0
Chimpanzee voice prints? Insights from transfer learning experiments from human voices—0
Brain Tumor Detection Using Deep Learning Approaches—0
Quantifying the value of information transfer in population-based SHM—0
Brain Tumor Classification on MRI in Light of Molecular Markers—0
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
Quantifying the value of positive transfer: An experimental case study—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning—0
Adaptive Trajectory Prediction via Transferable GNN—0
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN—0
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer—0
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains—0
Claim Detection in Biomedical Twitter Posts—0
Claim extraction from text using transfer learning.—0
Quantum Federated Learning With Quantum Networks—0
Brain-mediated Transfer Learning of Convolutional Neural Networks—0
ClaRet -- A CNN Architecture for Optical Coherence Tomography—0
Class-Aware Adversarial Transformers for Medical Image Segmentation—0
Class-based Subset Selection for Transfer Learning under Extreme Label Shift—0
Class Conditional Alignment for Partial Domain Adaptation—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