“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher.

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Self-Training With Noisy Student Improves ImageNet Classification

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.

Meta-Learning in Neural Networks: A Survey

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on the crowded scene analysis have been reported, which covered different aspects including crowd motion pattern learning, crowd behavior and activity analyses, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.

Crowded Scene Analysis: A Survey

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art.

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Learning to Self-Train for Semi-Supervised Few-Shot Classification

Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. Previous approaches build models on datasets which do not simulate the real-world data well (e.g., small scale, insignificant variance, etc.). Existing models may rely on auxiliary information, which prevents these anti-spoofing solutions from generalizing well in practice. In this paper, we present a data collection solution along with a data synthesis technique to simulate digital medium-based face spoofing attacks, which can easily help us obtain a large amount of training data well reflecting the real-world scenarios. Through exploiting a novel Spatio-Temporal Anti-Spoof Network (STASN), we are able to push the performance on public face anti-spoofing datasets over state-of-the-art methods by a large margin. Since the proposed model can automatically attend to discriminative regions, it makes analyzing the behaviors of the network possible.We conduct extensive experiments and show that the proposed model can distinguish spoof faces by extracting features from a variety of regions to seek out subtle evidences such as borders, moire patterns, reflection artifacts, etc.

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Face Anti-Spoofing: Model Matters, so Does Data

Conditional access system (CAS) is one of the key techniques in digital television (DTV) broadcast, which is used to charge the subscriber for subscribing fee by scrambling the program information. Scrambling algorithm and key distribution are the most important parts for CAS. In this paper, we proposed a new key distribution scheme based on hierarchical access control for conditional access system in digital television broadcast. Our key distribution scheme can greatly reduce the encrypting computation and acquire higher efficiency and security. Moreover, the proposed scheme is more flexible in processing joining and leaving of subscriber, which is very important for service provider to manage the subscriber.

Key distribution based on hierarchical access control for conditional access system in DTV broadcast

With the expansion of TV channels in digital era, viewers are exposed to information overload. Consequently, personalized guide systems are necessary to automatically customize program listings as well as to enable viewers to navigate and search programs. This paper proposes a personalized TV system running on standalone set-top box (STB) compliant with multimedia home platform (MHP) model. Besides the traditional functions, such as program navigation and search, this system is characterized by recommending program with high preferences on strategies of explicit and implicit feedback. Aiming at low end STB platform, we have deployed the personalized system in the MHP-compliant solution based on enhanced broadcasting profile. This paper also presents the implementing details and optimized strategies involved in information retrieving and program recommending. The experimental result shows that the proposed system presents a good performance.

A personalized TV guide system compliant with MHP

The field of video coding has been exploring the compact representation of video data, where perceptual redundancies in addition to signal redundancies are removed for higher compression. Many research efforts have been dedicated to modeling the human visual system's characteristics. The resulting models have been integrated into video coding frameworks in different ways. Among them, coding enhancements with the just noticeable distortion (JND) model have drawn much attention in recent years due to its significant gains. A common application of the JND model is the adjustment of quantization by a multiplying factor corresponding to the JND threshold. In this paper, we propose an alternative perceptual video coding method to improve upon the current H.264/advanced video control (AVC) framework based on an independent JND-directed suppression tool. This new tool is capable of finely tuning the quantization using a JND-normalized error model. To make full use of this new rate distortion adjustment component the Lagrange multiplier for rate distortion optimization is derived in terms of the equivalent distortion. Because the H.264/AVC integer discrete cosine transform (DCT) is different from classic DCT, on which state-of-the-art JND models are computed, we analytically derive a JND mapping formula between the integer DCT domain and the classic DCT domain which permits us to reuse the JND models in a more natural way. In addition, the JND threshold can be refined by adopting a saliency algorithm in the coding framework and we reduce the complexity of the JND computation by reusing the motion estimation of the encoder. Another benefit of the proposed scheme is that it remains fully compliant with the existing H.264/AVC standard. Subjective experimental results show that significant bit saving can be obtained using our method while maintaining a similar visual quality to the traditional H.264/AVC coded video.

Shibao Zheng

Papers

Learning to Self-Train for Semi-Supervised Few-Shot Classification

Face Anti-Spoofing: Model Matters, so Does Data

Key distribution based on hierarchical access control for conditional access system in DTV broadcast

A personalized TV guide system compliant with MHP

H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression