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Differentiable architecture search (DARTS) has gained significant attention amongst neural architecture search approaches due to its effectiveness in finding competitive network architectures with affordable computational complexity. However, DARTS&#39; search space is designed such that even a randomly sampled architecture performs reasonably well. Moreover, due to the complexity of search architectural building block or cell, it is unclear whether these are certain operations or the cell topology that contributes most to achieving higher final accuracy. In this work, we dissect the DARTS&#39;s search space to understand which components are most effective in producing better architectures. Our experiments show that: (1) Good architectures can be discovered regardless of the search network depth; (2) Separable convolution with 3x3 kernel is the most effective operation in this search space; and (3) The cell topology also has substantial effect on the accuracy. Based on these insights, we propose an efficient search approach referred to as eDARTS, which searches on a pre-specified cell having good topology with increased attention to important operations, using a shallow search supernet. Moreover, we propose some optimizations for eDARTS that significantly speed up the search as well as alleviate the well known skip connection aggregation problem of DARTS. eDARTS achieves an error rate of 2.53% on CIFAR-10 using a 3.1M parameters model whereas the search cost is less than 30 minutes.

ICPR 2020

Operation and Topology Aware Differentiable Architecture Search

convolutional neural networks

image classification

neural architecture search

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Differentiable architecture search (DARTS) has gained significant attention amongst neural architecture search approaches due to its effectiveness in finding competitive network architectures with affordable computational complexity. However, DARTS' search space is designed such that even a randomly sampled architecture performs reasonably well. Moreover, due to the complexity of search architectural building block or cell, it is unclear whether these are certain operations or the cell topology that contributes most to achieving higher final accuracy. In this work, we dissect the DARTS's search space to understand which components are most effective in producing better architectures. Our experiments show that: (1) Good architectures can be discovered regardless of the search network depth; (2) Separable convolution with 3x3 kernel is the most effective operation in this search space; and (3) The cell topology also has substantial effect on the accuracy. Based on these insights, we propose an efficient search approach referred to as eDARTS, which searches on a pre-specified cell having good topology with increased attention to important operations, using a shallow search supernet. Moreover, we propose some optimizations for eDARTS that significantly speed up the search as well as alleviate the well known skip connection aggregation problem of DARTS. eDARTS achieves an error rate of 2.53% on CIFAR-10 using a 3.1M parameters model whereas the search cost is less than 30 minutes.

poster

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. 



In the present study, we have considered a large number (27) of Indian languages for recognition from their speech signals of different sources. A dense convolutional network architecture (DenseNet) has been used for this classification task. Dynamic elimination of low energy frames from the input speech signal has been considered as a preprocessing operation. Mel-spectrogram of pre-processed speech signal is fed as input to the DenseNet architecture. Language recognition performance of this architecture has been compared with that of several state-of-the-art deep architectures which include a convolutional neural network (CNN), ResNet, CNN-BLSTM and DenseNet-BLSTM hybrid architectures. Additionally, we obtained recognition performances of a stacked BLSTM architecture fed with different sets of handcrafted features for comparison purpose. Simulations for both speaker independent and speaker dependent scenarios have been performed on two different standard datasets which include (i) IITKGP-MLILSC dataset of news clips in 27 different Indian languages and (ii) Linguistic Data Consortium (LDC) dataset of telephonic conversations in 5 different Indian languages. In each case, recognition performance of the DenseNet architecture along with Mel-spectrogram features has been found to be significantly better than all other frameworks implemented in this study.

DenseRecognition of Spoken Languages of Electrocardiograms

Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models.
The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques.
The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.

Personalized Models in Human Activity Recognition using Deep Learning


This paper considers arbitrary document detection performed on a mobile device. The classical contour-based approach often fails in cases featuring occlusion, complex background, or blur. The region-based approach, which relies on the contrast between object and background, does not have application limitations, however, its known implementations are highly resource-consuming. We propose a modification of the contour-based method, in which the competing contour location hypotheses are ranked according to the contrast between the areas inside and outside the border. In the experiments, such modification allows for the decrease of alternatives ordering errors by 40% and the decrease of the overall detection errors by 10%. The proposed method provides unmatched state-of-the-art performance on the open MIDV-500 dataset, and it demonstrates results comparable with state-of-the-art performance on the SmartDoc dataset.

Approach for Document Detection by Contours and Contrasts

We propose a novel joint framework for 3D face reconstruction (3DFR) that integrates facial attribute estimation (FAE) as an auxiliary task. One of the essential problems of 3DFR is to extract semantic facial features (e.g., Big Nose, High Cheekbones, and Asian) from in-the-wild 2D images, which is inherently involved with FAE. These two tasks, though heterogeneous, are highly relevant to each other. To achieve this, we leverage a Convolutional Neural Network to extract shared facial
representations for both shape decoder and attribute classifier. We further develop an in-batch hybrid-task training scheme that enables our model to learn from heterogeneous facial datasets jointly within a mini-batch. Thanks to the joint loss that provides supervision from both 3DFR and FAE domains, our model learns the correlations between 3D shapes and facial attributes, which benefit both feature extraction and shape inference. Quantitative evaluation and qualitative visualization results confirm the effectiveness and robustness of our joint framework.

Learning Semantic Representation via Joint 3D Face Reconstruction and Facial Attribute Estimation

During the process of space travelling and space landing, the spacecraft attitude estimation is the indispensable work for navigation. Since there are not enough satellites for GPS-like localization in space, the computer vision technique is adopted to address the issue. The most crucial task for localization is the extraction of correspondences. In computer vision, optical flow estimation is often used for finding correspondences between images. As the deep neural network being more popular in recent years, FlowNet2 has played a vital role which achieves great success. In this paper, we present PA-FlowNet, an end-to-end pose-auxiliary optical flow network which can use the predicted relative camera pose to improve the performance of optical flow. PA-FlowNet is composed of two sub-networks, the foreground-attention flow network and the pose regression network. The foreground-attention flow network is constructed by FlowNet2 model and modified with the proposed foreground-attention approach. We introduced this approach with the concept of curriculum learning for foreground-background segmentation to avoid backgrounds from resulting in flow prediction error. The pose regression network is used to regress the relative camera pose as an auxiliary for increasing the accuracy of the flow estimation. In addition, to simulate the test environment for spacecraft pose estimation, we construct a 64K moon model and simulate aerial photography with various attitudes to generate Moon64K dataset in this paper. PA-FlowNet significantly outperforms all existing methods on the proposed Moon64K dataset. Furthermore, we also predict the relative camera pose via proposed PA-FlowNet and accomplish the remarkable performance.

PA-FlowNet: Pose-Auxiliary Optical Flow Network for Spacecraft Relative Pose Estimation

Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

Class-Incremental Learning with Topological Schemas of Memory Spaces

Selecting Useful Knowledge from Previous Tasks for Future Learning in a Single Network

Facial expression synthesis has achieved remarkable advances with the advent of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly generate photo-realistic results as long as the testing data distribution is close to the training data distribution. The quality of GAN results significantly degrades when testing images are from a slightly different distribution. Moreover, recent work has shown that facial expressions can be synthesized by changing localized face regions. In this work, we propose a pixel-based facial expression synthesis method in which each output pixel observes only one input pixel. The proposed method achieves good generalization capability by leveraging only a few hundred training images. Experimental results demonstrate that the proposed method performs comparably well against state-of-the-art GANs on in-dataset images and significantly better on out-of-dataset images. In addition, the proposed model is two orders of magnitude smaller which makes it suitable for deployment on resource-constrained devices.

Pixel-based Facial Expression Synthesis

Visual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations through reinforcement learning. Unfortunately, collecting the required experience in the real world requires the deployment of a robotic platform, which is expensive and time-consuming. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. Despite the advantages they offer, simulators present a limited realism in terms of appearance and physical dynamics, leading to navigation policies that do not generalize in the real world. In this paper, we propose a tool based on the Habitat simulator which exploits real world images of the environment, together with sensor and actuator
noise models, to produce more realistic navigation episodes. We perform a range of experiments to assess the ability of such policies to generalize using virtual and real-world images, as well as observations transformed with unsupervised domain
adaptation approaches. We also assess the impact of sensor and actuation noise on the navigation performance and investigate whether it allows to learn more robust navigation policies. We show that our tool can effectively help to train and evaluate
navigation policies on real-world observations without running navigation episodes in the real world.

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Operation and Topology Aware Differentiable Architecture Search

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DenseRecognition of Spoken Languages of Electrocardiograms

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