In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximazes the lower bound of its marginalized log-likelihood. Then, according to the new probabilistic model, we design an algorithm which acts consistently during train and test. However, inference becomes computationally inefficient. To reduce memory and computational cost, we propose Stochastic Batch Normalization -- an efficient approximation of proper inference procedure. This method provides us with a scalable uncertainty estimation technique. We demonstrate the performance of Stochastic Batch Normalization on popular architectures (including deep convolutional architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.

To tackle the problem of saliency detection in images, we propose to learn adaptive mid-level features to represent image local information, and present an efficient way to calculate multi-scale and multi-level saliency maps. With the simple k-means algorithm, we learn adaptive low-level filters to convolve the image to produce response maps as the low-level features, which intrinsically capture texture and color information simultaneously. We adopt additional threshold and pooling techniques to generate mid-level features for more robustness in image local representation. Then, we define a set of hand-crafted filters, at multiple scales and multiple levels, to calculate local contrasts and result in several intermediate saliency maps, which are finally fused into the resultant saliency map with vision prior. Benefiting from these filters, the resultant saliency map not only captures subtle textures within the object, but also discovers the overall salient object in the image. Since both feature learning and saliency map calculation contain the convolution operation, we unify the two stages into one framework within a deep architecture. Through experiments over challenging benchmarks, we demonstrate the effectiveness of the proposed method.