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Found 5 papers, 3 papers with code
Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors
Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels
In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective.
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach
Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning.
Meta Soft Label Generation for Noisy Labels
In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples.
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
Because of these practical challenges, label noise is a common problem in real-world datasets, and numerous methods to train deep neural networks with label noise are proposed in the literature.
