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A PCA-like Autoencoder [article]

Saïd Ladjal, Alasdair Newson, Chi-Hieu Pham
2019 arXiv   pre-print
We demonstrate the results of this autoencoder on simple geometric shapes, and find that the algorithm indeed finds a meaningful representation in the latent space.  ...  This means that subsequent interpolation in the latent space has meaning with respect to the geometric properties of the images.  ...  Learning processes in an asymmetric threshold network. PhD thesis, Paris VI, 1987.  ... 
arXiv:1904.01277v1 fatcat:2qavywqk5fd2tnon7lxyb7f324

Processsing Simple Geometric Attributes with Autoencoders [article]

Alasdair Newson, Andrés Almansa, Yann Gousseau, Saïd Ladjal
2019 arXiv   pre-print
In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position.  ...  We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes.  ...  We believe that, in order to understand more complicated synthesis situations, it is necessary to first understand how these networks process simple attributes.  ... 
arXiv:1904.07099v1 fatcat:oc7fqgdkkfckbahyrzbcqxdtcy

Assessing Dataset Bias in Computer Vision [article]

Athiya Deviyani
2022 arXiv   pre-print
A biased dataset is a dataset that generally has attributes with an uneven class distribution.  ...  We also found that training on geometrically transformed images lead to a similar performance with a much quicker training time.  ...  F1 Geometric Acc. F1 Var. Autoencoder Acc. F1 StarGAN Acc.  ... 
arXiv:2205.01811v1 fatcat:nerm3uxlbngqfbi7fsll6zjtre

Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs [article]

Daniel T. Chang
2019 arXiv   pre-print
In this paper we discuss adapting tiered graph autoencoders for use with PyTorch Geometric, for both the deterministic tiered graph autoencoder model and the probabilistic tiered variational graph autoencoder  ...  Tiered latent representations and latent spaces for molecular graphs provide a simple but effective way to explicitly represent and utilize groups (e.g., functional groups), which consist of the atom (  ...  In this paper, we adapt tiered graph autoencoders for use with PyG.  ... 
arXiv:1908.08612v1 fatcat:fxrwzj7wlfaknipmjtgq6swv3u

An Emotional Recognition System for Facial Expressions with Surface Common Features

Maierdan Maimaitimin, Keigo Watanabe, Shoichi Maeyama
2016 International Journal on Smart Material and Mechatronics  
A geometric attribute map that is a surface common feature is obtained from such 3D point cloud data.  ...  As a result, the CNN with pre-training on surface common features outperforms the hand-crafted descriptors in the same experimental condition.  ...  . The processing of point cloud data with m points is calculated as the following steps: The difference between two adjacent normal vectors is defined as the distance vector D  , which can represent  ... 
doi:10.20342/ijsmm.3.2.192 fatcat:tncqhmgxnbculhbyjw2hsixuie

Autoencoder-based image processing framework for object appearance modifications

Krzysztof Ślot, Paweł Kapusta, Jacek Kucharski
2020 Neural computing & applications (Print)  
We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps.  ...  Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder.  ...  Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest in relation to this article.  ... 
doi:10.1007/s00521-020-04976-7 fatcat:jq6oglopezg63ecqi6kcacrmsa

Geometric Understanding of Deep Learning [article]

Na Lei, Zhongxuan Luo, Shing-Tung Yau, David Xianfeng Gu
2018 arXiv   pre-print
In this work, we give a geometric view to understand deep learning: we show that the fundamental principle attributing to the success is the manifold structure in data, namely natural high dimensional  ...  Then we show for any deep neural network with fixed architecture, there exists a manifold that cannot be learned by the network.  ...  The topological constraint implies that autoencoder can only learn manifolds with simple topologies, or a local chart of the whole manifold.  ... 
arXiv:1805.10451v2 fatcat:d2dbdlkqqnavxg5ajqtxalxa6q

An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry

Wei Wu, Guangmin Hu, Fucai Yu
2021 Symmetry  
Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes.  ...  Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding  ...  Some methods [14] , in essence, separate the processing of structural information and attribute information in the process of learning embeddings and then obtain the embeddings of attribute networks by  ... 
doi:10.3390/sym13050905 fatcat:5e5336nc6zhibfmsn57jlfxqs4

Revisiting Bayesian autoencoders with MCMC

Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky
2022 IEEE Access  
This has been addressed with variational autoencoders so far.  ...  This motivates further applications of the Bayesian autoencoder framework for other deep learning models.  ...  This work is licensed under a Creative Commons Attribution 4.0 License.  ... 
doi:10.1109/access.2022.3163270 fatcat:eibediyxyzc45hn6hyssbllxcy

Autoencoders for Level Generation, Repair, and Identification

Julian Togelius, Christoffer Holmgard, Aaron Isaksen, Rishabh Jain
2016 Computational Creativity & Games Workshop (CCGW)  
In this paper we argue for the use of autoencoders in game content generation, recognition and repair, and describe proof-of-concept implementations of autoencoders for these tasks for Super Mario Bros  ...  Autoencoders are neural networks for unsupervised learning and dimensionality reduction which have recently been used for generating and modeling images.  ...  Torch provides faster and more efficient processing of complex matrix computations attributed to it's underlying GPU-based implementation for hardware accelerated parallel processing.  ... 

A Deep Evaluator for Image Retargeting Quality by Geometrical and Contextual Interaction

Bin Jiang, Jiachen Yang, Qinggang Meng, Baihua Li, Wen Lu
2018 IEEE Transactions on Cybernetics  
In the perception process of retargeted image, both the simple geometrical changes and complex content maintaining should be considered.  ...  In simple terms, finding the keys for IRQA is a different issue with IQA.  ... 
doi:10.1109/tcyb.2018.2864158 pmid:30183651 fatcat:ocjk7guh2jgb5kn7xp6mznvcjq

Characterizing the impact of geometric properties of word embeddings on task performance [article]

Brendan Whitaker, Denis Newman-Griffis, Aparajita Haldar, Hakan Ferhatosmanoglu, Eric Fosler-Lussier
2019 arXiv   pre-print
Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.  ...  However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored.  ...  However, precedent for capturing geometric structure with autoencoders (Li et al., 2017b) suggests that this is a viable model for our analysis.  ... 
arXiv:1904.04866v1 fatcat:nbrt7idig5fddfedbdrll23uhy

Multilinear Autoencoder for 3D Face Model Learning

Victoria Fernandez Abrevaya, Stefanie Wuhrer, Edmond Boyer
2018 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)  
With the increase of 3D scan databases available for training, a growing challenge lies in the ability to learn generative face models that effectively encode shape variations with respect to desired attributes  ...  Given a set of 3D face scans with annotation labels for the desired attributes, e.g. identities and expressions, our method learns an expressive multilinear model that decouples shape changes due to the  ...  In other words, a simple geometric loss can lose the ability to decouple the different modes of variation, which impacts the expressiveness of the model.  ... 
doi:10.1109/wacv.2018.00007 dblp:conf/wacv/AbrevayaWB18 fatcat:i2na56uchbehbft4ab4jtryltu

Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

Hongzhe Liu, Weicheng Zheng, Cheng Xu, Teng Liu, Min Zuo, Mariko Nakano-Miyatake
2020 Mathematical Problems in Engineering  
In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders  ...  Figure 2 : 2 Simple process of landmark detection.  ...  As illustrated in Figure 1 , it diagrams the new framework of restoring the jaw and mouth part occluded by an occlusion. e specific regression process details can be seen in DRDA, and simple process can  ... 
doi:10.1155/2020/4589260 fatcat:dl72cim6djgp3dhyt6es3j4xb4

Multi-Task Shape Optimization Using a 3D Point Cloud Autoencoder as Unified Representation

Thiago Rios, Bas van Stein, Thomas Back, Bernhard Sendhoff, Stefan Menzel
2021 IEEE Transactions on Evolutionary Computation  
Here, we apply a 3D point cloud autoencoder to map the representations from the Cartesian to a unified design representation: the latent space of the autoencoder.  ...  The transfer of latent space features between design representations allows the reconstruction of shapes with interpolated characteristics and maintenance of common parts, which potentially improves the  ...  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.  ... 
doi:10.1109/tevc.2021.3086308 fatcat:alqxgqscpvc7fk6dc26wbn23re
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