We study the query-based attack against image retrieval to evaluate its
robustness against adversarial examples under the black-box setting, where the
adversary only has query access to the top-k ranked unlabeled images from the
database. Compared with query attacks in image classification, which produce
adversaries according to the returned labels or confidence score, the challenge
becomes even more prominent due to the difficulty in quantifying the attack
effectiveness on the partial retrieved list. In this paper, we make the first
attempt in Query-based Attack against Image Retrieval (QAIR), to completely
subvert the top-k retrieval results. Specifically, a new relevance-based loss
is designed to quantify the attack effects by measuring the set similarity on
the top-k retrieval results before and after attacks and guide the gradient
optimization. To further boost the attack efficiency, a recursive model
stealing method is proposed to acquire transferable priors on the target model
and generate the prior-guided gradients. Comprehensive experiments show that
the proposed attack achieves a high attack success rate with few queries
against the image retrieval systems under the black-box setting. The attack
evaluations on the real-world visual search engine show that it successfully
deceives a commercial system such as Bing Visual Search with 98% attack success
rate by only 33 queries on average.

本研究通过 Quantifying the Attack Effects on the Partial Retrieved List 方法，提出了第一种针对黑盒情况下图片检索的基于查询的攻击（QAIR）方法，并采用了新的基于相关性的损失设计和递归模型窃取方法，通过少量请求，成功地欺骗了 Bing 视觉搜索这样的商业系统。

QAIR: 实用的查询效率高的黑盒攻击图像检索

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

Deep Spiking Neural Networks (SNNs) present optimization difficulties for
gradient-based approaches due to discrete binary activation and complex
spatial-temporal dynamics. Considering the huge success of ResNet in deep
learning, it would be natural to train deep SNNs with residual learning.
Previous Spiking ResNet mimics the standard residual block in ANNs and simply
replaces ReLU activation layers with spiking neurons, which suffers the
degradation problem and can hardly implement residual learning. In this paper,
we propose the spike-element-wise (SEW) ResNet to realize residual learning in
deep SNNs. We prove that the SEW ResNet can easily implement identity mapping
and overcome the vanishing/exploding gradient problems of Spiking ResNet. We
evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and
show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in
both accuracy and time-steps. Moreover, SEW ResNet can achieve higher
performance by simply adding more layers, providing a simple method to train
deep SNNs. To our best knowledge, this is the first time that directly training
deep SNNs with more than 100 layers becomes possible. Our codes are available
at this https URL

本文提出了基于 SEW ResNet 的残差学习算法来实现深度 SNNs 的直接训练，并通过在 ImageNet、DVS Gesture 和 CIFAR10-DVS 数据集上的实验表明 SEW ResNet 相对于目前最先进的神经网络直接训练方法在准确性和时间步长方面均有更好的表现。

脉冲神经网络中的深度剩余学习

Deep Residual Learning in Spiking Neural Networks

The deep learning community has devised a diverse set of methods to make
gradient optimization, using large datasets, of large and highly complex models
with deeply cascaded nonlinearities, practical. Taken as a whole, these methods
constitute a breakthrough, allowing computational structures which are quite
wide, very deep, and with an enormous number and variety of free parameters to
be effectively optimized. The result now dominates much of practical machine
learning, with applications in machine translation, computer vision, and speech
recognition. Many of these methods, viewed through the lens of algorithmic
differentiation (AD), can be seen as either addressing issues with the gradient
itself, or finding ways of achieving increased efficiency using tricks that are
AD-related, but not provided by current AD systems.
The goal of this paper is to explain not just those methods of most relevance
to AD, but also the technical constraints and mindset which led to their
discovery. After explaining this context, we present a "laundry list" of
methods developed by the deep learning community. Two of these are discussed in
further mathematical detail: a way to dramatically reduce the size of the tape
when performing reverse-mode AD on a (theoretically) time-reversible process
like an ODE integrator; and a new mathematical insight that allows for the
implementation of a stochastic Newton's method.

本文介绍了深度学习社群已经开发的一系列方法，包括梯度优化、算法微分、机器学习和随机牛顿法，并详细讨论了其中两种方法的数学细节。