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.

翻译：随着深层学习的到来,在过去几年里,提出新方法或改进现有方法的作品数量成倍增长。在这种情景中,“非常深”的模式正在出现,一旦期望它们提取更多的内在和抽象特征,同时支持更好的表现。然而,这些模型会因渐渐消失的问题而受到影响,即,在它们的浅层,背面再造法值太接近于零,最终导致学习停滞。通过在所谓的深层残余学习框架内在各层之间建立“短期联系”来克服了这个问题。然而,一个非常受欢迎的深层学习技术,即“深信仰网络”在处理歧视性任务时仍然受到梯度消失的影响。因此,本文件提出了“残余信仰网络”,认为信息强化层逐层可以改进特征提取和知识保留,从而支持更好的歧视性表现。在三个公共数据集上进行的实验表明,它在二进制图像分类任务方面是稳健健的。