Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-theart performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://github.com/google/lecam-gan.

/pdf/regularizing-generative-adversarial-networks-under-limited-16sk0ieoyw.pdf

Regularizing Generative Adversarial Networks under Limited Data

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds

http://manuscript.elsevier.com/S0167739X22001170/pdf/S0167739X22001170.pdf

Pedestrian safety using the Internet of Things and sensors: Issues, challenges, and open problems

Object detection has gained great improvements with the advances of convolutional neural networks and the availability of large amounts of accurate training data. Though the amount of data is increasing significantly, the quality of data annotations is not guaranteed from the existing crowd-sourcing labeling platforms. In addition to noisy category labels, imprecise bounding box annotations are commonly existed for object detection data. When the quality of training data degenerates, the performance of the typical object detectors is severely impaired. In this paper, we propose a Meta-Refine-Net (MRNet) to train object detectors from noisy category labels and imprecise bounding boxes. First, MRNet learns to adaptively assign lower weights to proposals with incorrect labels so as to suppress large loss values generated by these proposals on the classification branch. Second, MRNet learns to dynamically generate more accurate bounding box annotations to overcome the misleading of imprecisely annotated bounding boxes. Thus, the imprecise bounding boxes could impose positive impacts on the regression branch rather than simply be ignored. Third, we propose to refine the imprecise bounding box annotations by jointly learning from both the category and the localization information. By doing this, the approximation of ground-truth bounding boxes is more accurate while the misleading would be further alleviated. Our MRNet is model-agnostic and is capable of learning from noisy object detection data with only a few clean examples (less than 2%). Extensive experiments on PASCAL VOC 2012 and MS COCO 2017 demonstrate the effectiveness and efficiency of our method.

Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes

Recently, the rich features extracted by deep learning models have been widely used under the correlation filter tracking framework and achieved great success. However, the features in different layers are often combined with fixed weights, which do not consider the different importance of different layers. In this paper, we propose a novel tracking method which can adaptively tune the weights of the convolutional responses obtained by features in different layers. We propose two adaptive weighting strategies, i.e. the cosine weighting and quadratic optimization weighting, adaptively assigning weights to each submodel, and combining multiple view submodels. Moreover, Normalized Peak Value is used to estimate the tracking reliability. Experimental results demonstrate that the proposed adaptive fusion based method can achieve comparable performance to several state-of-the-art approaches on public dataset.

Correlation filters with adaptive convolution response fusion for object tracking

It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks. Code are released at https://github.com/youjiangxu/FaMUS.

/pdf/faster-meta-update-strategy-for-noise-robust-deep-learning-54w20uwctk.pdf

Faster Meta Update Strategy for Noise-Robust Deep Learning

It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks.

Youjiang Xu

Papers

Faster Meta Update Strategy for Noise-Robust Deep Learning

Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes

Faster Meta Update Strategy for Noise-Robust Deep Learning