Results 221 to 230 of about 28,763 (261)

A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes. [PDF]

Head Face Med
Yadalam PK, Pawar AR, Natarajan PM, Ardila CM.   +3 more
europepmc   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Attention-enhanced corn disease diagnosis using few-shot learning and VGG16. [PDF]

MethodsX
Rani R, Sahoo J, Bellamkonda S, Kumar S.
europepmc   +1 more source

Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain. [PDF]

Biology (Basel)
Uzhinskiy A.
europepmc   +1 more source

Hybrid Modeling of the Reversed-Phase Chromatographic Purification of an Oligonucleotide: Few-Shot Learning From Differentiable Physics Solver-in-the-Loop. [PDF]

Biotechnol Bioeng
Chen YC, Fioretti I, Lin DQ, Sponchioni M.   +3 more
europepmc   +1 more source

Few-shot learning for inference in medical imaging with subspace feature representations. [PDF]

PLoS One
Liu J, Fan K, Cai X, Niranjan M.
europepmc   +1 more source

Few-Shot Learning in Wi-Fi-Based Indoor Positioning. [PDF]

Biomimetics (Basel)
Xie F, Lam SH, Xie M, Wang C.
europepmc   +1 more source

Explainable AI for Chronic Kidney Disease Prediction in Medical IoT: Integrating GANs and Few-Shot Learning. [PDF]

Bioengineering (Basel)
Rezk NG, Alshathri S, Sayed A, Hemdan EE.   +3 more
europepmc   +1 more source

Few-Shot Learning With a Strong Teacher

IEEE Transactions on Pattern Analysis and Machine Intelligence
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner that can learn from few-shot examples to generate a classifier. Typically, the few-shot learner is constructed or meta-trained by sampling multiple few-shot tasks in turn and ...
Han-Jia Ye, Lu Ming
exaly   +4 more sources

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Variational Few-Shot Learning

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
We propose a variational Bayesian framework for enhancing few-shot learning performance. This idea is motivated by the fact that single point based metric learning approaches are inherently noise-vulnerable and easy-to-be-biased. In a nutshell, stochastic variational inference is invoked to approximate bias-eliminated class specific sample ...
Jian Zhang 0079, Chenglong Zhao, Bingbing Ni, Minghao Xu, Xiaokang Yang 0001   +4 more
openaire   +1 more source