Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resourcelimited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the fullprecision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bitwidth reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the stateof-the-art accuracy.

Spoofing using photographs or videos is one of the most common methods of attacking face recognition and verification systems. In this paper, we propose a real-time and nonintrusive method based on the diffusion speed of a single image to address this problem. In particular, inspired by the observation that the difference in surface properties between a live face and a fake one is efficiently revealed in the diffusion speed, we exploit antispoofing features by utilizing the total variation flow scheme. More specifically, we propose defining the local patterns of the diffusion speed, the so-called local speed patterns, as our features, which are input into the linear SVM classifier to determine whether the given face is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies diverse malicious attacks regardless of the medium of the image, e.g., paper or screen. Moreover, the proposed method does not require any specific user action. Experimental results on various data sets show that the proposed method is effective for face liveness detection as compared with previous approaches proposed in studies in the literature.

No abstract

No abstract

No abstract