Comments on "Noise injection into inputs in back propagation learning"
Yves Grandvalet, Stéphane Canu · IEEE Transactions on Systems Man and Cybernetics · 1995
The generalization capacity of neural networks learning from examples is important. Several authors showed experimentally that training a neural network with noise injected inputs could improve its generalization abilities. In the original paper (ibid., vol. 22, no. 3. p. 436-40, 1992), Matsuoka explained this fact in a formal way, claiming that using noise injected inputs is equivalent to reduce the sensitivity of the network. However, the author states that an error in Matsuoka's calculations lead him to inadequate conclusions. This paper corrects these calculations and conclusions.>