The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology | Paper Citation Record

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Unresolved cited work

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This paper cites The code utilizes the common libraries: cv2, tensorflow, and keras (see Github project page for associated code, https://github.com/caleb-nasa/gt-CAE).

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology The code utilizes the common libraries: cv2, tensorflow, and keras (see Github project page for associated code, https://github.com/caleb-nasa/gt-CAE)

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Unresolved cited work

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology in-painting

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This paper cites Error bars correspond to 1-sigma errors on the mean.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Error bars correspond to 1-sigma errors on the mean

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology out of bounds

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology best possible

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology MultiMAE: Multi-modal Multi-task Masked Autoencoders

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This paper cites Machine Learning with Applications 6, 100134.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Machine Learning with Applications 6, 100134

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology JGR Solid Earth 126, e2020JB021589

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This paper cites A guide to convolution arithmetic for deep learning.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology A guide to convolution arithmetic for deep learning

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Masked Autoencoders As Spatiotemporal Learners

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology ConvMAE: Masked Convolution Meets Masked Autoencoders

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This paper cites IEEE, New Orleans, LA, USA, pp.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology IEEE, New Orleans, LA, USA, pp

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Using Pre-Training Can Improve Model Robustness and Uncertainty

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This paper cites Denoising Diffusion Probabilistic Models.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Denoising Diffusion Probabilistic Models

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology SpectralGPT: Spectral Remote Sensing Foundation Model

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This paper cites Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 2506–2517.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 2506–2517

Reference 32

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This paper cites Computer Vision and Image Understanding 203, 103147.

The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Computer Vision and Image Understanding 203, 103147

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 3900–3918

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Adam: A Method for Stochastic Optimization

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Unresolved cited work

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Earth and Space Science 10, e2022EA002278

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Nat Astron 8, 8–9

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Icarus 419, 115612

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Attention Is All You Need

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Monthly Notices of the Royal Astronomical Society 513, 1581–1599

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Diffusion Models as Masked Autoencoders

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Revealing the Dark Secrets of Masked Image Modeling

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology latent”, features in data as multi-dimensional vectors (a “latent representation

Reference 1972

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Monthly Notices of the Royal Astronomical Society 211, 111–124

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Proceedings of the IEEE 86, 2278–2324

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Proceedings of the IEEE 88, 569–587

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology up sampling

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Reference 2018

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology EPSC2020-773)

Reference 2020

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Reference 2021

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Reference 2022

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology MGMAE: Motion Guided Masking for Video Masked Autoencoding

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The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Monthly Notices of the Royal Astronomical Society 529, 732–747

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