Stable training of autoencoders for hyperspectral unmixing

• URL: http://arxiv.org/abs/2109.13748v1
• Date: Tue, 28 Sep 2021 14:07:24 GMT
• Title: Stable training of autoencoders for hyperspectral unmixing
• Authors: Kamil Ksi\k{a}\.zek, Przemys{\l}aw G{\l}omb, Micha{\l} Romaszewski, Micha{\l} Cholewa and Bartosz Grabowski
• Abstract summary: We show that training of autoencoders for unmixing is highly dependent on weights initialisation. Some sets of weights lead to degenerate or low performance solutions, introducing negative bias in expected performance. In this work we present the results of experiments investigating autoencoders' stability, verifying the dependence of reconstruction error on initial weights and exploring conditions needed for successful optimisation of autoencoder parameters.
• Score: 2.099922236065961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks, autoencoders in particular, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances). Unmixing is needed for effective hyperspectral analysis and classification. However, as we show in this paper, the training of autoencoders for unmixing is highly dependent on weights initialisation. Some sets of weights lead to degenerate or low performance solutions, introducing negative bias in expected performance. In this work we present the results of experiments investigating autoencoders' stability, verifying the dependence of reconstruction error on initial weights and exploring conditions needed for successful optimisation of autoencoder parameters.

Related papers

• Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum [56.37522020675243]
  We provide the first proof of convergence for normalized error feedback algorithms across a wide range of machine learning problems. We show that due to their larger allowable stepsizes, our new normalized error feedback algorithms outperform their non-normalized counterparts on various tasks.
  arXiv  Detail & Related papers  (2024-10-22T10:19:27Z)
• PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction [5.06136344261226]
  CNN-based methods for data fidelity enhancement rely on low-fidelity data patterns and distributions during the training phase. Our proposed model - Physics-informed Residual Diffusion - demonstrates the capability to elevate the quality of data from both standard low-fidelity inputs. Experimental results have shown that our approach can effectively reconstruct high-quality outcomes for two-dimensional turbulent flows without requiring retraining.
  arXiv  Detail & Related papers  (2024-04-12T11:45:51Z)
• Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders [0.565395466029518]
  We develop hyperspectral unmixing algorithms based on autoencoder neural networks. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing methods.
  arXiv  Detail & Related papers  (2024-03-07T14:27:08Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness [68.97830259849086]
  Most datasets only capture a simpler subproblem and likely suffer from spurious features. We study adversarial robustness - a local generalization property - to reveal hard, model-specific instances and spurious features. Unlike in other applications, where perturbation models are designed around subjective notions of imperceptibility, our perturbation models are efficient and sound. Surprisingly, with such perturbations, a sufficiently expressive neural solver does not suffer from the limitations of the accuracy-robustness trade-off common in supervised learning.
  arXiv  Detail & Related papers  (2021-10-21T07:28:11Z)
• Quantized Proximal Averaging Network for Analysis Sparse Coding [23.080395291046408]
  We unfold an iterative algorithm into a trainable network that facilitates learning sparsity prior to quantization. We demonstrate applications to compressed image recovery and magnetic resonance image reconstruction.
  arXiv  Detail & Related papers  (2021-05-13T12:05:35Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss [4.56877715768796]
  This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they are further modeled with the Multinomial Mixture Model. Experiments are performed on both real and synthetic datasets.
  arXiv  Detail & Related papers  (2020-12-12T16:49:01Z)
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection [1.5675763601034223]
  A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed. One of the examples is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum. We explore two solutions to this challenge by incorporating minimally prototypical simulation into the learning.
  arXiv  Detail & Related papers  (2020-09-04T14:02:15Z)
• Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
  empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
  arXiv  Detail & Related papers  (2020-06-11T09:58:01Z)

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.