在机器学习中,使用基于梯度的学习方法和反向传播训练人工神经网络时,会遇到梯度消失的问题。在这种方法中,每个神经网络的权值在每次迭代训练时都得到一个与误差函数对当前权值的偏导数成比例的更新。问题是,在某些情况下,梯度会极小,有效地阻止权值的改变。在最坏的情况下,这可能会完全阻止神经网络进一步的训练。作为问题原因的一个例子,传统的激活函数,如双曲正切函数的梯度在范围(0,1),而反向传播通过链式法则计算梯度。这样做的效果是将n个这些小数字相乘来计算n层网络中“前端”层的梯度,这意味着梯度(误差信号)随着n的增加呈指数递减,而前端层的训练非常缓慢。

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With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, "very deep" models were emerging, once they were expected to extract more intrinsic and abstract features while supporting a better performance. However, such models suffer from the gradient vanishing problem, i.e., backpropagation values become too close to zero in their shallower layers, ultimately causing learning to stagnate. Such an issue was overcome in the context of convolution neural networks by creating "shortcut connections" between layers, in a so-called deep residual learning framework. Nonetheless, a very popular deep learning technique called Deep Belief Network still suffers from gradient vanishing when dealing with discriminative tasks. Therefore, this paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining, that support better discriminative performance. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification.

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