[13] T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” The Journal of Machine Learning Research, vol. 22, no. 1, pp. 10 882–11 005, 2021. [14] J. Hoffmann...
This work explore approach for image denoising of received by CMOS sensor. Proposed pipeline solves the problem of unsupervised training neural network architectures for image denoising which uses datasets without clean data. This approach bases on theoretical background about image restoration proposed by...
论文信息 论文标题:Domain-Adversarial Training of Neural Networks论文作者:Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain论文来源:JMLR 2016论文地址:download
论文地址:Domain-Adversarial Training of Neural Networks Adversarial Discriminative Domain Adaptation 代码地址:pytorch-domain-adaptation 自2018年以来,领域自适应发展地非常迅速,这很大一部分得益于下游应用的广泛需要。深度学习作为一门数据驱动的科学,在任何领域都需要大量的标注数据来训练。暂时还没有能力使得无监督或...
^Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The journal of machine learning research, 17(1), 2096-2030.https://www.jmlr.org/papers/volume17/15-239/15-239.pdf...
Thus the nature of unsupervised learning is invariant to different training criteria. As a result we propose a new technique called “REBA” for the unsupervised training of deep neural networks. In contrast to Hinton’s conventional approach to the learning of restricted Boltzmann machine, which ...
Our research addresses this issue by implementing Neural Cellular Automata (NCA) training directly on smartphones for accessible X-ray lung segmentation . We confirm the practicality and feasibility of deploying and training these advanced models on five Android devices, improving medical diagnostics ...
Incremental Unsupervised Domain-Adversarial Training of Neural Networks In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is ...
Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but wit
This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed...