This paper reviews recent studies in emerging directions of understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always an Achilles' heel of deep neural networks. At present, deep neural networks obtain a h...

Understanding the underlying processes of aesthetic perception is one of the ultimate goals in empirical aesthetics. While deep learning and convolutional neural networks (CNN) already arrived in the area of aesthetic rating of art and photographs, only little attempts have been made to apply CNNs as the underlying model for aesthetic perception. The information processing architecture of CNNs ...

In this work, we explore an innovative strategy for image denoising by using convolutional neural networks (CNN) to learn pixel-distribution from noisy data. By increasing CNN’s width with large reception fields and more channels in each layer, CNNs can reveal the ability of learning pixel-distribution, which is a prior excising in many different types of noise. The key to our approach is a dis...

Convolutional neural networks (CNNs) have proven effective for image processing tasks, such as object recognition and classification. Recently, CNNs have been enhanced with concepts of attention, similar to those found in biology. Much of this work on attention has focused on effective serial spatial processing. In this paper, I introduce a simple procedure for applying feature-based attention ...

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights. We give an exact connection to a par...

This paper proposes an algorithmic optimization for the feature extractors of biologically inspired Convolutional Neural Networks (CNNs). CNNs are successfully used for different visual pattern recognition applications such as OCR, face detection and object classification. These applications require complex networks exceeding 100,000 interconnected computational nodes. To reduce the computation...

Convolutional Neural Networks (CNNs) have achieved breakthrough results on many machine learning tasks. However, training CNNs is computationally intensive. When the size of training data is large and the depth of CNNs is high, as typically required for attaining high classification accuracy, training a model can take days and even weeks. In this work, we propose SpeeDO (for Open DEEP learning ...

Convolutional neural networks (CNNs) have had much success in the past decade on a many complex vision tasks, successfully passing human benchmarks on object recognition, image segmentation, and video classification [1, 2, 3]. Much of the success of CNNs can be attributed to their ability to automatically learn feature maps and scale with large datasets [5]. More recently, deep CNNs have been a...

We introduce a novel scheme to train binary convolutional neural networks (CNNs) – CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power co...

We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of...