SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing

• URL: http://arxiv.org/abs/2102.05713v1
• Date: Wed, 10 Feb 2021 19:37:52 GMT
• Title: SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing
• Authors: Gurpreet Singh, Soumyajit Gupta, Matthew Lease, Clint Dawson
• Abstract summary: We show that a two-layer autoencoder (SCA-Net) achieves error metrics that are scales apart ($10-5)$ from previously reported values. We also show that SCA-Net, based upon a bi-orthogonal representation, performs a self-correction when the the number of endmembers are over-specified.
• Score: 3.918940900258555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Linear Mixture Model for hyperspectral datasets involves separating a mixed pixel as a linear combination of its constituent endmembers and corresponding fractional abundances. Both optimization and neural methods have attempted to tackle this problem, with the current state of the art results achieved by neural models on benchmark datasets. However, our review of these neural models show that these networks are severely over-parameterized and consequently the invariant endmember spectra extracted as decoder weights has a high variance over multiple runs. All of these approaches require substantial post-processing to satisfy LMM constraints. Furthermore, they also require an exact specification of the number of endmembers and specialized initialization of weights from other algorithms like VCA. Our work shows for the first time that a two-layer autoencoder (SCA-Net), with $2FK$ parameters ($F$ features, $K$ endmembers), achieves error metrics that are scales apart ($10^{-5})$ from previously reported values $(10^{-2})$. SCA-Net converges to this low error solution starting from a random initialization of weights. We also show that SCA-Net, based upon a bi-orthogonal representation, performs a self-correction when the the number of endmembers are over-specified. We show that our network formulation extracts a low-rank representation that is bounded below by a tail-energy and can be computationally verified. Our numerical experiments on Samson, Jasper, and Urban datasets demonstrate that SCA-Net outperforms previously reported error metrics for all the cases while being robust to noise and outliers.

Related papers

• Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators [78.64101336150419]
  Predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a temporalittext model. We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation.
  arXiv  Detail & Related papers  (2024-08-09T17:05:45Z)
• Stochastic Approximation Approach to Federated Machine Learning [0.0]
  This paper examines Federated learning (FL) in a Approximation (SA) framework. FL is a collaborative way to train neural network models across various participants or clients. It is observed that the proposed algorithm is robust and gives more reliable estimates of the weights.
  arXiv  Detail & Related papers  (2024-02-20T12:00:25Z)
• Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization [15.517787031620864]
  The territory of LASSO is extended to two-layer ReLU neural networks, a fashionable and powerful nonlinear regression model. We show that the LASSO estimator can stably reconstruct the neural network and identify $mathcalSstar$ when the number of samples scales logarithmically. Our theory lies in an extended Restricted Isometry Property (RIP)-based analysis framework for two-layer ReLU neural networks.
  arXiv  Detail & Related papers  (2023-05-07T13:05:09Z)
• FeDXL: Provable Federated Learning for Deep X-Risk Optimization [105.17383135458897]
  We tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing algorithms are applicable. The challenges for designing an FL algorithm for X-risks lie in the non-decomability of the objective over multiple machines and the interdependency between different machines.
  arXiv  Detail & Related papers  (2022-10-26T00:23:36Z)
• WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects [3.1806743741013648]
  We present WaferSegClassNet (WSCN), a novel network based on encoder-decoder architecture. WSCN performs simultaneous classification and segmentation of both single and mixed-type wafer defects. We are the first to show segmentation results on the MixedWM38 dataset.
  arXiv  Detail & Related papers  (2022-07-03T05:46:19Z)
• Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis [121.9821494461427]
  We show how to significantly reduce the number of neurons required for two-layer ReLU networks. We also prove new lower bounds that improve upon prior work, and that under certain assumptions, are best possible.
  arXiv  Detail & Related papers  (2022-06-26T06:51:31Z)
• Combinatorial optimization for low bit-width neural networks [23.466606660363016]
  Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting. We show that a combination of greedy coordinate descent and this novel approach can attain competitive accuracy on binary classification tasks.
  arXiv  Detail & Related papers  (2022-06-04T15:02:36Z)
• Hierarchical autoregressive neural networks for statistical systems [0.05156484100374058]
  We propose a hierarchical association of physical degrees of freedom, for instance spins, to neurons which replaces it with the scaling with the linear extent $L$ of the system. We demonstrate our approach on the two-dimensional Ising model by simulating lattices of various sizes up to $128 times 128$ spins, with time benchmarks reaching lattices of size $512 times 512$.
  arXiv  Detail & Related papers  (2022-03-21T13:55:53Z)
• Towards an Understanding of Benign Overfitting in Neural Networks [104.2956323934544]
  Modern machine learning models often employ a huge number of parameters and are typically optimized to have zero training loss. We examine how these benign overfitting phenomena occur in a two-layer neural network setting. We show that it is possible for the two-layer ReLU network interpolator to achieve a near minimax-optimal learning rate.
  arXiv  Detail & Related papers  (2021-06-06T19:08:53Z)
• Solving Mixed Integer Programs Using Neural Networks [57.683491412480635]
  This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each.
  arXiv  Detail & Related papers  (2020-12-23T09:33:11Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.