MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources

Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations on the target domain, domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the distribution difference among different sub-domains even in the same source, or cannot reflect the varying optimal weighting during different training stages. In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. Specifically, C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features. Further, our dynamic instance-level weighting mechanisms can assign the optimal weights to different source samples in each training stage. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches. Our source code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.Comment: Accepted by WWW 202

Peixin Yang's Economic thought and its Outcomes in the 1980s, Early in China's Reform, and China's Reform Miracle

During the initial period of reform, China's prudent handling of the money—price relationship while simultaneously bringing inflation and unemployment under control had a far-reaching influence on economic theory as well as practical significance. The country's successful experience constituted a miracle beyond the dreams of authoritative international financial authorities and experts. The famous American Nobel Prize winner, the economist Milton Friedman, once said that anyone who could explain China's success could win the Nobel Prize. In today's complex world, with its recurrent financial turmoil and intensified threat of war, China's successful experience in the early phases of reform has important significance for developing socialism with Chinese characteristics and maintaining the country's economic and financial security and the interests of the great mass of the people. We should sum up this experience to strengthen confidence in our institutions, our path, and our theory

Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing

Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of small objects. This work proposed a framework for end-to-end policy learning with tactile feedback and sim-to-real transfer, which achieved fine in-hand manipulation that controls the pose of a thin cylindrical object, such as a long stick, to track various continuous trajectories through multiple contacts of three fingertips of a dexterous robot hand with tactile sensor arrays. We estimated the central contact position between the stick and each fingertip from the high-dimensional tactile information and showed that the learned policies achieved effective manipulation performance with the processed tactile feedback. The policies were trained with deep reinforcement learning in simulation and successfully transferred to real-world experiments, using coordinated model calibration and domain randomization. We evaluated the effectiveness of tactile information via comparative studies and validated the sim-to-real performance through real-world experiments.Comment: 10 pages, 12 figures, submitted to Transaction on Mechatronic

AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections

Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.Comment: SIGGRAPH Asia 2023. Project Page: https://yuewuhkust.github.io/AniPortraitGAN

Computational Emotion Analysis From Images: Recent Advances and Future Directions

Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.Comment: Accepted chapter in the book "Human Perception of Visual Information Psychological and Computational Perspective

The impact of two drying methods on the quality of high-moisture rice: Poster

In this experiment, freshly harvested rice was dried by natural and mechanical methods. For natural drying, paddy rice was spread on a cement floor under a shelter at a thickness of 4cm, and it was turned twice a day. At a temperature of 19.3°C and a relative humidity of 58.8%, a total of 28 days was needed to reduce the water content from 23.11 to 14.38%. For mechanical drying, the Guwang 5HXG-15B circulating dryer was used, drying temperature was set to 42°C, and it took a total of 5 hours to reduce the water content from 23.1 to 11.8%. The changes in spore count, fatty acid value, germination rate, waist burst rate, whole polished rice rate, and taste value of rice mold after drying were studied. The results showed that compared with mechanical drying, the drying rate of air-dried rice was slower, and the number of mold spores increased from 0.65×105/g to 3.05×105/g, a 3.7 times increase. The number of mold spores in dried rice was not significant. Dried rice fatty acid value of 25.1mg/100g for natural drying was higher than the value of 19.9mg/100g for mechanical drying. High temperature affected rice seed vigor: mechanically dried rice germination rate was 58.0%, far lower than the 87.5% for natural drying. The blasting rate, polished rice rate, and taste value of mechanically dried rice were 5.33%, 57.9%, and 83.7, respectively, which was 2.33%, 58.9%, and 89.3 for naturally-dried rice. The processing quality and taste quality were even worse. Therefore, the drying process of the optimized circulation dryer should be further adjusted to reduce its impact on rice processing quality and taste quality.In this experiment, freshly harvested rice was dried by natural and mechanical methods. For natural drying, paddy rice was spread on a cement floor under a shelter at a thickness of 4cm, and it was turned twice a day. At a temperature of 19.3°C and a relative humidity of 58.8%, a total of 28 days was needed to reduce the water content from 23.11 to 14.38%. For mechanical drying, the Guwang 5HXG-15B circulating dryer was used, drying temperature was set to 42°C, and it took a total of 5 hours to reduce the water content from 23.1 to 11.8%. The changes in spore count, fatty acid value, germination rate, waist burst rate, whole polished rice rate, and taste value of rice mold after drying were studied. The results showed that compared with mechanical drying, the drying rate of air-dried rice was slower, and the number of mold spores increased from 0.65×105/g to 3.05×105/g, a 3.7 times increase. The number of mold spores in dried rice was not significant. Dried rice fatty acid value of 25.1mg/100g for natural drying was higher than the value of 19.9mg/100g for mechanical drying. High temperature affected rice seed vigor: mechanically dried rice germination rate was 58.0%, far lower than the 87.5% for natural drying. The blasting rate, polished rice rate, and taste value of mechanically dried rice were 5.33%, 57.9%, and 83.7, respectively, which was 2.33%, 58.9%, and 89.3 for naturally-dried rice. The processing quality and taste quality were even worse. Therefore, the drying process of the optimized circulation dryer should be further adjusted to reduce its impact on rice processing quality and taste quality

ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.Comment: Accepted by AAAI 202