Italy

In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an improper way might collapse the latent representations of the in-class data and thus their reconstruction, which would lead to performance deterioration. Moreover, to properly measure the reconstruction error of high-dimensional data, a metric is required that captures high-level semantics of the data. To this end, we propose Discriminative Compact AE (DCAE) that learns both compact and collapse-free latent representations of the in-class data, thereby reconstructing them both finely and exclusively. In DCAE, (a) we force a compact latent space to bijectively represent the in-class data by reconstructing them through internal discriminative layers of generative adversarial nets. (b) Based on the deep encoder&#39;s vulnerability to open set risk, out-class instances are encoded into the same compact latent space and reconstructed poorly without sacrificing the quality of in-class data reconstruction. (c) In inference, the reconstruction error is measured by a novel metric that computes the dissimilarity between a query and its reconstruction based on the class semantics captured by the internal discriminator. Extensive experiments on public image datasets validate the effectiveness of our proposed model on both novelty and adversarial example detection, delivering state-of-the-art performance.

ICPR 2020

Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection

novelty detection

anomaly detection

deep learning

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 one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an improper way might collapse the latent representations of the in-class data and thus their reconstruction, which would lead to performance deterioration. Moreover, to properly measure the reconstruction error of high-dimensional data, a metric is required that captures high-level semantics of the data. To this end, we propose Discriminative Compact AE (DCAE) that learns both compact and collapse-free latent representations of the in-class data, thereby reconstructing them both finely and exclusively. In DCAE, (a) we force a compact latent space to bijectively represent the in-class data by reconstructing them through internal discriminative layers of generative adversarial nets. (b) Based on the deep encoder's vulnerability to open set risk, out-class instances are encoded into the same compact latent space and reconstructed poorly without sacrificing the quality of in-class data reconstruction. (c) In inference, the reconstruction error is measured by a novel metric that computes the dissimilarity between a query and its reconstruction based on the class semantics captured by the internal discriminator. Extensive experiments on public image datasets validate the effectiveness of our proposed model on both novelty and adversarial example detection, delivering state-of-the-art performance.

poster

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used
for different classification tasks, these methods have some shortcomings.
The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity.
Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the `2;1 constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

Linear Embedding by Joint Robust Discriminant Analysis and Inter-class Sparsity

A simple but effective deep clustering method.

(Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

Point cloud processing and 3D shape understanding are
challenging tasks for which deep learning techniques have
demonstrated great potentials. Still further progresses are
essential to allow artificial intelligent agents to
interact with the real world. In many practical conditions
the amount of annotated data may be limited and integrating
new sources of knowledge becomes crucial to support
autonomous learning. Here we consider several scenarios
involving synthetic and real world point clouds where
supervised learning fails due to data scarcity and large
domain gaps. We propose to enrich standard feature
representations by leveraging self-supervision through a
multi-task model that can solve a 3D puzzle while learning
the main task of shape classification or part segmentation.
An extensive analysis investigating few-shot, transfer
learning and cross-domain settings shows the effectiveness
of our approach with state-of-the-art results.

Joint Supervised and Self-Supervised Learning for 3D Real World Challenges

We propose Fast Direction-Search-Based Subspace Clustering (Fast DiSC), a scalable and robust framework for large scale clustering that rests upon representations derived from projections on computed data-dependent directions. To advance scalability, a Direction Search in Data Partitions (DiSPart) procedure performs direction-search in small partitions of sampled data, followed by retrieval using Retrieval using Direction-based Projections (ReDiP) to cluster out-of-sample data using projections on the computed directions and the associated clustering. A theoretical study establishes the correctness of the direction-based clustering framework and that of direction-search in small partitions. In turn, Fast DiSC can provably realize substantial speedups while maintaining high accuracy.

Scalable Direction-Search-Based Approach to Subspace Clustering

Intersection scenarios provide the most complex trafﬁc situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufﬁcient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.

Holistic Grid Fusion Based Stop Line Estimation

Survival data is often collected in medical applications from a heterogeneous population of patients. While in the past, popular survival models focused on modeling the average effect of the covariates on survival outcomes, rapidly advancing sensing and information technologies have provided opportunities to further model the heterogeneity of the population as well as the non-linearity of the survival risk. With this motivation, we propose a new semi-parametric Bayesian Survival Rule List model in this paper. Our model derives a rule-based decision-making approach, while within the regime defined by each rule, survival risk is modelled via a Gaussian process latent variable model. Markov Chain Monte Carlo with a nested Laplace approximation on the Gaussian process posterior is used to search over the posterior of the rule lists efficiently. The use of ordered rule lists enables us to model heterogeneity while keeping the model complexity in check. Performance evaluations on a synthetic heterogeneous survival dataset and a real world sepsis survival dataset demonstrate the effectiveness of our model.

GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

The Mean Teacher (MT) model of Tarvainen and Valpola has shown favorable performance on several semi- supervised benchmark datasets. MT maintains a teacher model’s weights as the exponential moving average of a student model’s weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning.

Local Clustering with Mean Teacher for Semi-supervised learning

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.

Relative Feature Importance

In feature embedding, the recovery of associated
discriminative information in the reduced subspace is critical
for downstream classifiers. In this study, a supervised feature
embedding method is proposed inspired by the well-known word
embedding technique, word2vec. Proposed embedding method
is implemented as representative learning of rank-based neighborhoods.
The notion of context words in word2vec is extended
into neighboring instances within a given window. Neighborship
is defined using ranks of instances rather than their values so
that regions with different densities are captured properly. Each
sample is represented by a unique one-hot vector whereas its
neighbors are encoded by several two-hot vectors. The two-hot
vectors are identical for neighboring samples of the same class. A
feed-forward neural network with a continuous projection layer,
then learns the mapping from one-hot vectors to multiple two-hot
vectors. The hidden layer determines the reduced subspace for
the train samples. The obtained transformation is then applied
on test data to find a lower-dimensional representation. Proposed
method is tested in classification problems on 10 UCI data
sets. Experimental results confirm that the proposed method is
effective in finding a discriminative representation of the features
and outperforms several supervised

Supervised Feature Embedding for Classification by Learning Rank-based Neighborhoods

Context is known to be one of crucial factors effecting the performance improvement of semantic segmentation. However, state-of-the-art segmentation models built upon fully convolutional networks are inherently weak in encoding contextual information because of stacked local operations such as convolution and pooling. Failing to capture context leads to inferior segmentation performance. Despite many context modules have been proposed to relieve this problem, they still operate in a local manner or use the same contextual information in different positions (due to upsampling). In this paper, we introduce the idea of Multi-Direction Convolution (MDC)—a novel operator capable of encoding rich contextual information. This operator is inspired by an observation that the standard convolution only slides along the spatial dimension (x, y direction) where the channel dimension (z direction) is fixed, which renders slow growth of the receptive field (RF). If considering the channel-fixed convolution to be one-direction, MDC is multi-direction in the sense that MDC slides along both spatial and channel dimensions, i.e., it slides along x, y when z is fixed, along x, z when y is fixed, and along y, z when x is fixed. In this way, MDC is able to encode rich contextual information with the fast increase of the RF. Compared to existing context modules, the encoded context is position-sensitive because no upsampling is required. MDC is also efficient and easy to implement. It can be implemented with few standard convolution layers with permutation. We show through extensive experiments that MDC effectively and selectively enlarges the RF and outperforms existing contextual modules on two standard benchmarks, including Cityscapes and PASCAL VOC2012.

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