CAT:Collaborative Adversarial Training

Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained by different strategies. By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances. For example, a sample instance can be correctly classified by a model trained using standard adversarial training (AT) but not by a model trained using TRADES, and vice versa. Based on this observation, we propose a collaborative adversarial training framework to improve the robustness of neural networks. Specifically, we use different adversarial training methods to train robust models and let models interact with their knowledge during the training process. Collaborative Adversarial Training (CAT) can improve both robustness and accuracy. Extensive experiments on various networks and datasets validate the effectiveness of our method. CAT achieves state-of-the-art adversarial robustness without using any additional data on CIFAR-10 under the Auto-Attack benchmark. Code is available at https://github.com/liuxingbin/CAT.Comment: Tech repor

Latent Feature Relation Consistency for Adversarial Robustness

Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial examples which add human-imperceptible adversarial noise to natural examples. This limits the application of DNN in security-critical fields. To alleviate this problem, we first conducted an empirical analysis of the latent features of both adversarial and natural examples and found the similarity matrix of natural examples is more compact than those of adversarial examples. Motivated by this observation, we propose \textbf{L}atent \textbf{F}eature \textbf{R}elation \textbf{C}onsistency (\textbf{LFRC}), which constrains the relation of adversarial examples in latent space to be consistent with the natural examples. Importantly, our LFRC is orthogonal to the previous method and can be easily combined with them to achieve further improvement. To demonstrate the effectiveness of LFRC, we conduct extensive experiments using different neural networks on benchmark datasets. For instance, LFRC can bring 0.78\% further improvement compared to AT, and 1.09\% improvement compared to TRADES, against AutoAttack on CIFAR10. Code is available at https://github.com/liuxingbin/LFRC.Comment: Tech repor

DLIP: Distilling Language-Image Pre-training

Vision-Language Pre-training (VLP) shows remarkable progress with the assistance of extremely heavy parameters, which challenges deployment in real applications. Knowledge distillation is well recognized as the essential procedure in model compression. However, existing knowledge distillation techniques lack an in-depth investigation and analysis of VLP, and practical guidelines for VLP-oriented distillation are still not yet explored. In this paper, we present DLIP, a simple yet efficient Distilling Language-Image Pre-training framework, through which we investigate how to distill a light VLP model. Specifically, we dissect the model distillation from multiple dimensions, such as the architecture characteristics of different modules and the information transfer of different modalities. We conduct comprehensive experiments and provide insights on distilling a light but performant VLP model. Experimental results reveal that DLIP can achieve a state-of-the-art accuracy/efficiency trade-off across diverse cross-modal tasks, e.g., image-text retrieval, image captioning and visual question answering. For example, DLIP compresses BLIP by 1.9x, from 213M to 108M parameters, while achieving comparable or better performance. Furthermore, DLIP succeeds in retaining more than 95% of the performance with 22.4% parameters and 24.8% FLOPs compared to the teacher model and accelerates inference speed by 2.7x

Benchmarking Component Analysis of Remanent Magnetization Curves With a Synthetic Mixture Series: Insight into the Reliability of Unmixing Natural Samples

Geological samples often contain several magnetic components associated with different geological processes. Component analysis of remanent magnetization curves has been widely applied to decompose convoluted information. However, the reliability of commonly used methods is poorly assessed as independent verification is rarely available. For this purpose, we designed an experiment using a series of mixtures of two endmembers to benchmark unmixing methods for isothermal remanent magnetization (IRM) acquisition curves. First‐order reversal curves (FORC) diagrams were analyzed for comparison. It is demonstrated that the parametric method, which unmixes samples using specific probability distributions, may result in biased estimates. In contrast, an endmember‐based IRM unmixing approach yielded better quantitative results, which are comparable to the results obtained by FORC analysis based on principle component analysis (FORC‐PCA). We demonstrate that endmember‐based methods are in principle more suitable for unmixing a collection of samples with common endmembers; however, the level of decomposition will vary depending on the difference between the true endmembers that are associated with distinctive processes and the empirical endmembers used for unmixing. When it is desired to further decompose endmembers, the parametric unmixing approach remains a valuable means of inferring their underlying components. We illustrate that the results obtained by endmember‐based and parametric methods can be quantitatively combined to provide improved unmixing results at the level of parametric model distributions.The work was supported by the National Natural Science Foundation of China (41621004 and 41904070) and the Strategic Priority Research Program of Chinese Academy of Sciences (XDB18010000). This study was also supported by the National Institute of Polar Research (NIPR) through Advanced Project (KP‐7 and KP306) and JSPS KAKENHI grants (15K13581, 16H04068, 17H06321, and 18K13638). X. Z. acknowledges the Australian Research Council Discovery Projects DP200100765 and the National Natural Science Foundation of China (grant 41920104009) for financial supports

A Resonant Lorentz-Force Magnetometer Exploiting Blue Sideband Actuation to Enhance Sensitivity and Resolution

This paper reports a miniaturized resonant Lorentz-force magnetometer that exploits blue-sideband actuation to attain a better sensitivity and resolution. The resonant magnetometer consists of a double-ended tuning fork (DETF) resonator with cavity slots to optimize thermoelastic dissipation, as well as a Lorentz-force generator structure to transduce the magnetic force to the axial of the resonator. The proposed device demonstrates a Lorentz-force sensitivity of 5.5 mV/nN, a noise floor of 1.25 μV/ √ Hz, and a resolution of 0.23 pN/ √ Hz. In comparison with a conventional drive scheme, the blue- sideband actuation achieves approximately two orders of magnitude improvement regarding sensitivity and resolution than that of the amplitude modulation (AM) readout and 3.6-fold enhancement than that of the frequency modulation (FM) readout. The results affirm the merit of the novel excitation method and provide solid evidence of its effectiveness in practical applications

A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors

Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers