We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.MegaFace show that our MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs for face verification. Related WorkTuning deep neural architectures to strike an optimal balance between accuracy and performance has been an area of active research for the last several years [3]. For common visual recognition tasks, many efficient architectures have been proposed recently [1,2,3,9]. Some efficient architectures can be trained from scratch. For example, SqueezeNet ([9]) uses a bottleneck approach to design a very small network and achieves AlexNet-level [10] accuracy on ImageNet [11, 12] with 50x fewer parameters (i.e., 1.25 million). MobileNetV1 [1] uses depthwise separable convolutions to build lightweight deep neural networks, one of which, i.e., MobileNet-160 (0.5x), achieves 4% better accuracy on ImageNet than SqueezeNet at about the same size. ShuffleNet [2] utilizes pointwise group convolution and channel shuffle operation to reduce computation cost and achieve higher efficiency than MobileNetV1. MobileNetV2 [3] architecture is based on an inverted residual structure with linear bottleneck and improves the state-of-the-art performance of mobile models on multiple tasks and benchmarks. The mobile NASNet [13] model, which is an architectural search result with reinforcement learning, has much more complex structure and much more actual inference time on mobile devices than MobileNetV1, ShuffleNet, and MobileNetV2. However, these lightweight basic architectures are not so accurate for face verification when trained from scratch (see Table 2). Accurate lightweight architectures specifically designed for face verification have been rarely researched. [14] presents a light CNN framework to learn a compact embedding on the large-scale face data, in which the Light CNN-29 model achieves 99.33% face verification accuracy on LFW with 12.6 million parameters. Compared with MobileNetV1, Light CNN-29 is not lightweight for mobile and embedded platform. Light CNN-4 and Ligh...

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Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm.

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Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.