Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs

Convolutional neural networks (CNNs) are powerful tools in Deep Learning mainly due to their ability to exploit the translational symmetry present in images, as they are equivariant to translations. Other datasets present different types of symmetries (e.g. rotations), or lie on the sphere S2 (e.g. cosmological maps, omni-directional images, 3D models, ...). It is therefore of interest to design architectures that exploit the structure of the data and are equivariant to the 3D rotation group SO(3). Different architectures were designed to exploit these symmetries, such as 2D convolutions on planar projections, convolutions on the SO(3)group, or convolutions on graphs. The DeepSphere model approximates the sphere with a graph and performs graph convolutions. In this study, DeepSphere is evaluated against other spherical CNNs on different tasks.While the SO(3) convolution is equivariant to all rotations in SO(3), the graph convolutionis only equivariant to the rotations in S2 and invariant to the third rotation. Our experiments on SHREC-17 (a 3D shape retrieval task) show that DeepSphere achieves the same performance while being 40 times faster to train than Cohen et al. and 4 times faster than Esteves et al. Equivariance to the third rotation is an unnecessary price to pay. In order to prove these results, DeepSphere was tested on the similar dataset ModelNet40 (a shape classification task), and similar results as obtained by Esteves et al. were achieved. The odd behaviour with rotations (the performance worsens in presence of rotation perturbations) may be inherent to the task and the classes, instead of the models or the choice of the sampling scheme. Finally, regression tasks (both global and dense) were performed on GHCN-daily to prove the flexibility of DeepSphere with a non-hierarchical and irregular sampling of the sphere. The SCNN performed better than simply learning on the time series for each nodes.

Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning

Gülcan Can

In this dissertation, we study visual analysis methods for complex ancient Maya writings. The unit sign of a Maya text is called glyph, and may have either semantic or syllabic significance. There are over 800 identified glyph categories, and over 1400 variations across these categories. To enable fast manipulation of data by scholars in Humanities, it is desirable to have automatic visual analysis tools such as glyph categorization, localization, and visualization. Analysis and recognition of glyphs are challenging problems. The same patterns may be observed in different signs but with different compositions. The inter-class variance can thus be significantly low. On the opposite, the intra-class variance can be high, as the visual variants within the same semantic category may differ to a large extent except for some patterns specific to the category. Another related challenge of Maya writings is the lack of a large dataset to study the glyph patterns. Consequently, we study local shape representations, both knowledge-driven and data-driven, over a set of frequent syllabic glyphs as well as other binary shapes, i.e. sketches. This comparative study indicates that a large data corpus and a deep network architecture are needed to learn data-driven representations that can capture the complex compositions of local patterns. To build a large glyph dataset in a short period of time, we study a crowdsourcing approach as an alternative to time-consuming data preparation of experts. Specifically, we work on individual glyph segmentation out of glyph-blocks from the three remaining codices (i.e. folded bark pages painted with a brush). With gradual steps in our crowdsourcing approach, we observe that providing supervision and careful task design are key aspects for non-experts to generate high-quality annotations. This way, we obtain a large dataset (over 9000) of individual Maya glyphs. We analyze this crowdsourced glyph dataset with both knowledge-driven and data-driven visual representations. First, we evaluate two competitive knowledge-driven representations, namely Histogram of Oriented Shape Context and Histogram of Oriented Gradients. Secondly, thanks to the large size of the crowdsourced dataset, we study visual representation learning with deep Convolutional Neural Networks. We adopt three data-driven approaches: assess- ing representations from pretrained networks, fine-tuning the last convolutional block of a pretrained network, and training a network from scratch. Finally, we investigate different glyph visualization tasks based on the studied representations. First, we explore the visual structure of several glyph corpora by applying a non-linear dimensionality reduction method, namely t-distributed Stochastic Neighborhood Embedding, Secondly, we propose a way to inspect the discriminative parts of individual glyphs according to the trained deep networks. For this purpose, we use the Gradient-weighted Class Activation Mapping method and highlight the network activations as a heatmap visualization over an input image. We assess whether the highlighted parts correspond to distinguishing parts of glyphs in a perceptual crowdsourcing study. Overall, this thesis presents a promising crowdsourcing approach, competitive data-driven visual representations, and interpretable visualization methods that can be applied to explore various other Digital Humanities datasets.

EPFL2017

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progress in deep learning, and in particular in convolutional neural networks (CNN). To design a CNN for a specific task, one common approach consists of adapting the heuristics from the pre-deep-learning era to the CNN domain. In the first part of this thesis, we introduce two methods that follow this approach: i) We build a covariance descriptor, i.e., a local descriptor that is suitable for texture recognition, to replace the first-order fully connected layers in an ordinary CNN, showing that such a descriptor yields state-of-the-art performance on many fine-grained image classification tasks with orders of magnitude fewer feature dimensions; ii) we develop a light-weight recurrent U-Net for image semantic segmentation, inspired by the biological eye saccadic movements, that yields real-time predictions on devices with limited computational resources. As most methods pre-dating automatic machine learning (AutoML), the two above-mentioned CNNs were human-designed. In the past few years, however, neural architecture search~(NAS), which aims to facilitate the design of deep networks for new tasks, has drawn an increasing attention. In this context, the weight-sharing approach, which consists of utilizing a super-net to encompass all possible architectures within a search space, has become a de facto standard in NAS because it enables the search to be done on commodity hardware. In the second part of this thesis, we then provide an in-depth study of recent weight-sharing NAS algorithms. First, we discover a phenomenon in the weight-sharing NAS training pipeline, which we dub multi-model forgetting, that negatively impacts the super-net quality, and propose a statistically motivated approach to address it. Subsequently, we find that (i) on average, many popular NAS algorithms perform similarly to a random architecture sampling policy; (ii) the widely-adopted weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process. We then further decouple weight sharing from the NAS sampling policy, and isolate 14 factors that play a key role in the success of super-net training. Finally, to improve the super-net quality, we propose a regularization term that aims to maximize the correlation between the performance rankings of the super-net and of the stand-alone architectures using a small set of landmark architectures.

EPFL2021

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Roberto Gerson De Albuquerque Azevedo, Pascal Frossard, Thomas Maugey

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omnidirectional images have specific spatial and statistical properties that can not be fully captured by current CNN models. Second, basic mathematical operations composing a CNN architecture, e.g., translation and sampling, are not well-defined on the sphere. In this paper, we study the learning of representation models for omnidirectional images and propose to use the properties of HEALPix uniform sampling of the sphere to redefine the mathematical tools used in deep learning models for omnidirectional images. In particular, we: i) propose the definition of a new convolution operation on the sphere that keeps the high expressiveness and the low complexity of a classical 2D convolution; ii) adapt standard CNN techniques such as stride, iterative aggregation, and pixel shuffling to the spherical domain; and then iii) apply our new framework to the task of omnidirectional image compression. Our experiments show that our proposed on-the-sphere solution leads to a better compression gain that can save 13.7% of the bit rate compared to similar learned models applied to equirectangular images. Also, compared to learning models based on graph convolutional networks, our solution supports more expressive filters that can preserve high frequencies and provide a better perceptual quality of the compressed images. Such results demonstrate the efficiency of the proposed framework, which opens new research venues for other omnidirectional vision tasks to be effectively implemented on the sphere manifold.