Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms

• URL: http://arxiv.org/abs/2104.10180v1
• Date: Tue, 20 Apr 2021 18:01:55 GMT
• Title: Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms
• Authors: Maxim Ziatdinov, Sergei Kalinin
• Abstract summary: We introduce a joint rotationally (and translationally) invariant variational autoencoder (j-trVAE) The performance of this method is validated on several synthetic data sets and extended to high-resolution imaging data of electron and scanning probe microscopy. We show that latent space behaviors directly comport to the known physics of ferroelectric materials and quantum systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in imaging from celestial objects in astronomy visualized via optical and radio telescopes to atoms and molecules resolved via electron and probe microscopes are generating immense volumes of imaging data, containing information about the structure of the universe from atomic to astronomic levels. The classical deep convolutional neural network architectures traditionally perform poorly on the data sets having a significant orientational disorder, that is, having multiple copies of the same or similar object in arbitrary orientation in the image plane. Similarly, while clustering methods are well suited for classification into discrete classes and manifold learning and variational autoencoders methods can disentangle representations of the data, the combined problem is ill-suited to a classical non-supervised learning paradigm. Here we introduce a joint rotationally (and translationally) invariant variational autoencoder (j-trVAE) that is ideally suited to the solution of such a problem. The performance of this method is validated on several synthetic data sets and extended to high-resolution imaging data of electron and scanning probe microscopy. We show that latent space behaviors directly comport to the known physics of ferroelectric materials and quantum systems. We further note that the engineering of the latent space structure via imposed topological structure or directed graph relationship allows for applications in topological discovery and causal physical learning.

Related papers

• Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization [3.386918190302773]
  Variational Autoencoders (VAEs) have emerged as powerful tools for identifying underlying factors of variation in image data. We introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales.
  arXiv  Detail & Related papers  (2024-08-01T01:48:46Z)
• Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
  Extended Vision techniques often pose a challenge in their interpretation. The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation. In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
  arXiv  Detail & Related papers  (2024-01-31T09:31:28Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• PyNanospacing: TEM image processing tool for strain analysis and visualization [0.0]
  This paper develops a Python code for TEM image processing that can handle a wide range of materials. It converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression. The tool is very generic and can significantly aid in analyzing material properties using TEM images.
  arXiv  Detail & Related papers  (2023-11-27T12:08:46Z)
• Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders [0.0]
  Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis. This article summarizes recent developments in VAEs, covering the basic principles and intuition behind the VAEs.
  arXiv  Detail & Related papers  (2023-03-30T03:16:27Z)
• Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis [0.0]
  We present a physics augmented machine learning method which disentangles factors of variability within the data. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data.
  arXiv  Detail & Related papers  (2023-02-08T17:35:38Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders [45.29194877564103]
  This work introduces a chart autoencoder with an asymmetric encoding-decoding process that can incorporate additional semi-supervised information such as class labels. We discuss the approximation power of such networks and derive a bound that essentially depends on the intrinsic dimension of the data manifold rather than the dimension of ambient space.
  arXiv  Detail & Related papers  (2022-08-22T19:58:03Z)
• Geometric Transformer for End-to-End Molecule Properties Prediction [92.28929858529679]
  We introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism.
  arXiv  Detail & Related papers  (2021-10-26T14:14:40Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Learning Bijective Feature Maps for Linear ICA [73.85904548374575]
  We show that existing probabilistic deep generative models (DGMs) which are tailor-made for image data, underperform on non-linear ICA tasks. To address this, we propose a DGM which combines bijective feature maps with a linear ICA model to learn interpretable latent structures for high-dimensional data. We create models that converge quickly, are easy to train, and achieve better unsupervised latent factor discovery than flow-based models, linear ICA, and Variational Autoencoders on images.
  arXiv  Detail & Related papers  (2020-02-18T17:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.