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Adversarial attacks have been a prevalent problem causing misclassification in machine learning models, with stochasticity being a promising direction towards greater robustness. However, stochastic networks frequently underperform compared to deterministic deep networks. In this work, we present a conceptually clear adaptive noise injection mechanism in combination with teacher-initialisation, which adjusts its degree of randomness dynamically through the computation of mini-batch statistics. This mechanism is embedded within a simple framework to obtain stochastic networks from existing deterministic networks. Our experiments show that our method is able to outperform prior baselines under white-box settings, exemplified through CIFAR-10 and CIFAR-100. Following which, we perform in-depth analysis on varying different components of training with our approach on the effects of robustness and accuracy, through the study of the evolution of decision boundary and trend curves of clean accuracy/attack success over differing degrees of stochasticity. We also shed light on the effects of adversarial training on a pre-trained network, through the lens of decision boundaries.

ICPR 2020

Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

adaptive stochasticity

stochastic networks

adversarial robustness

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


technical paper

In this paper, we present a distinct discriminant
canonical correlation analysis network (DDCCANet) based deep
information quality representation with application to image
classification. Specifically, to explore the sufficient discriminant
information between different data sets, the within-class and
between-class correlation matrices are employed and optimized
jointly. Moreover, different from the existing canonical correlation
analysis network (CCANet) and related algorithms, an
information theoretic descriptor, information quality (IQ), is
adopted to generate the deep-level feature representation for
image classification. Benefiting from the explored discriminant
information and IQ descriptor, it is potential to gain a more
effective deep-level representation from multi-view data sets,
leading to improved performance in classification tasks. To
demonstrate the effectiveness of the proposed DDCCANet, we
conduct experiments on the Olivetti Research Lab (ORL) face
database, ETH80 database and CIFAR10 database. Experimental
results show the superiority of the proposed solution on image
classification.

A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification


The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross-entropy loss, which is a simple yet effective application of information theory for classification problems. Based on this loss, many other loss functions have been proposed, e.g., by adding intra-class and inter-class constraints to enhance the discriminative ability of the learned features. However, these loss functions fail to consider the connections between the feature distribution and the model structure. Aiming at ad- dressing this problem, we propose a channel correlation loss (CC-Loss) that is able to constrain the specific relations be-tween classes and channels as well as maintain the intra-class and the inter-class separability. CC-Loss uses a channel attention module to generate channel attention of features for each sample in the training stage. Next, a Euclidean distance matrix is calculated to make the channel attention vectors associated with the same class become identical and to increase the difference between different classes. Finally, we obtain a feature embedding with good intra-class compactness and inter-class separability. Experimental results show that two different backbone models trained with the proposed CC-Loss outperform the state-of-the-art loss functions on three image classification datasets.

CC-LOSS: Channel Correlation Loss For Image Classification

Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. 
In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the predictions of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The source code is available at https://github.com/SunJiamei/few-shot-lrp-guided

Explanation-Guided Training for Cross-Domain Few-Shot Classification

Person re-identification aims to identify the same person from different cameras, which needs to integrate whole-body information and capture global correlation. However, convolutional neural network is able to only capture short-distance information because of the size of filters. Self-attention is introduced to capture long-distance correlation, but inner-product similarity calculation in self-attention mingles semantic response and semantic difference together. Semantic difference is more important for person re-identification, because it is robust to illumination without the effect of semantic response. However, we find the scale of norms measuring semantic response is much larger than angle measuring semantic difference by decoupling inner-product similarity into norms and angle. To balance the importance of semantic response and semantic difference in self-attention, we propose the decoupled self-attention module for person re-identification to make the most of self-attention. Extensive experiments show that the decoupled self-attention module obtains significant performance with easier convergence and stronger robustness.

Decoupled Self-attention Module for Person Re-identification


Creating a good looking chart for better visualization is time consuming.
There are plenty of well-designed charts on the Web, which are ideal references for imitation of chart style. However, stored as bitmap images, reference charts have hinder machine interpretation of style settings and thus difficult to be directly applied.
In this paper, we extract visual properties from reference chart images as style templates to restyle charts. We first construct a large-scale dataset of 187,059 chart images from real world data, labeled with predefined visual property values. Then we introduce an end-to-end learning network to extract the properties based on two image-encoding approaches. Furthermore, in order to capture spatial relationships of chart objects, which are crucial in solving the task, Experimental results show that our model significantly outperforms baseline models. By adding positional features, our model achieves better performance. Finally, we present the application for chart restyling based on our model.

Visual Style Extraction from Chart Images for Chart Restyling

From a spherical image, a gaze point, instead of
gaze vectors, can be estimated directly because a remote spherical
camera can observe a user’s face and a gaze target simultane-
ously. This paper investigates the problem of estimating a gaze
point in a spherical image from facial landmarks. In contrast with
the existing methods which usually assume gaze points move on
a narrow plane, the proposed method can cope with the situation
where gaze points vary in depth for a relatively wide field of view. (a) As shown in the results of comparative experiments, we find the
orthogonal coordinates of facial landmarks on a unit sphere is
a reasonable representation in comparison with spherical polar
coordinates; the cues of head pose is helpful to improve the
accuracy of gaze points. Consequently, the proposed method
achieves a performance on the accuracy of gaze points estimation
which is comparable to the state of the art methods.

Estimating Gaze Points from Facial Landmarks by a Remote Spherical Camera

A new base-derivative framework is proposed, where base refers to the original visible and infrared modalities, and derivative refers to the two auxiliary modalities that are derived from base. 
In the proposed framework, the double-modality cross-modal learning problem is reformulated as a four-modality one. After that, the images of all the base and derivative modalities are fed into the feature learning network. With the doubled input images, the learned person features become more discriminative.
Furthermore, the proposed framework is optimized by the enhanced intra- and cross-modality constraints with the assistance of two derivative modalities.

A Base-Derivative Framework for Cross-Modality RGB-Infrared Person Re-Identification

Deep neural network based object detection has become the cornerstone of many real-world applications. Along with this success comes concerns about its vulnerability to malicious attacks. To gain more insight into this issue, we propose a contextual camouflage attack (CCA for short) algorithm to influence the performance of object detectors. In this paper, we use an evolutionary search strategy and adversarial machine learning in interactions with a photo-realistic simulated environment to find camouflage patterns that are effective over a huge variety of object locations, camera poses, and lighting conditions. The proposed camouflages are validated effective to most of the state-of-the-art object detectors.

CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

An extreme learning machine (ELM) is a three-layered feed-forward neural network having untrained parameters, which are randomly determined before training. 
Inspired by the idea of ELM, a probabilistic untrained layer called a probabilistic-ELM (PELM) layer is proposed, and it is combined with a discriminative restricted Boltzmann machine (DRBM), which is a probabilistic three-layered neural network for solving classification problems. The proposed model is obtained by stacking DRBM on the PELM layer. The resultant model (i.e., multi-layered DRBM (MDRBM)) forms a probabilistic four-layered neural network. In MDRBM, the parameters in the PELM layer can be determined using Gaussian-Bernoulli restricted Boltzmann machine. Owing to the PELM layer, MDRBM obtains a strong immunity against noise in inputs, which is one of the most important advantages of MDRBM. Numerical experiments using some benchmark datasets, MNIST, Fashion-MNIST, Urban Land Cover, and CIFAR-10, demonstrate that MDRBM is superior to other existing models, particularly, in terms of the noise-robustness property (or, in other words, the generalization property).

Multi-Layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer


Recent advances in Handwritten Text Recognition and Document Layout Analysis make it possible to extract information from digitized documents and make them accessible beyond the archive shelves. But the reading order of the elements in those documents still is an open problem that has to be solved in order to provide that information with the correct structure. Most of the studies on the reading order task are rule-base approaches that focus on printed documents, while less attention has been paid to handwritten text documents. In this work we propose a new approach to automatically determine the reading order of text lines in handwritten text documents. The task is approached as a sorting problem where the order-relation operator is learned directly from examples. We demonstrate the effectiveness of our method on three different datasets.

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Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

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A Distinct Discriminant Canonical Correlation Analysis Network based Deep Information Quality Representation for Image Classification

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Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers