Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks
In this paper, we show that noise injection into inputs in unsupervised learning neural networks does not improve their performance as it does in supervised learning neural networks. Specifically, we show that training noise degrades the classification ability of a sparsely connected version of the...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
2012
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/94091 http://hdl.handle.net/10220/8195 |
| الوسوم: |
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| الملخص: | In this paper, we show that noise injection into inputs in unsupervised learning neural networks does not improve their performance as it does in supervised learning neural networks. Specifically, we show that training noise degrades the classification ability of a sparsely connected version of the Hopfield neural network, whereas the performance of a sparsely connected winner-take-all neural network does not depend on the injected training noise. |
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