Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

Leong, Oscar; Sakla, Wesam

doi:10.48550/arxiv.1910.10797

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…2: Examples of inverse imaging problems (IIPs). Individual images adapted from: Left: [18]; Right (top to bottom): first 1 ; second [24]; third [64]; fourth [65].…”

Section: Hand-crafted Priorsmentioning

confidence: 99%

“…-Deep Image Prior -Deep Decoder -Deep Prior [6], [7], [8], [9], [10], [11] [12] [13], [14] 2019 -Deep Image Prior -Untrained Neural Network Priors -Untrained Network Priors -Deep Prior -Deep Network Prior [15], [16] [17], [18] [19], [20] [21], [22] [23] 2020 -Deep Image Prior -Untrained Network Priors -Untrained Neural Networks -Deep Decoder -Deep Prior -Untrained Deep Neural Network -Untrained Neural Network Priors [24], [25] [26]…”

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confidence: 99%

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Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem. Recently, a new paradigm of training deep models using a single image, named untrained neural network prior (UNNP) has been proposed to solve a variety of inverse tasks, e.g., restoration and inpainting. Since then, many researchers have proposed various applications and variants of UNNP. In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research.

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“…2: Examples of inverse imaging problems (IIPs). Individual images adapted from: Left: [18]; Right (top to bottom): first 1 ; second [24]; third [64]; fourth [65].…”

Section: Hand-crafted Priorsmentioning

confidence: 99%

mentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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“…See[21] that tries to bridge the two regimes though 3. In a sense, this is inverting the discretization process in typical image formation.…”

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confidence: 99%

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors

Li¹,

Zhuang²,

Liang³

et al. 2021

Preprint

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Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data. We call these models, such as deep image prior and deep decoder, collectively as single-instance deep generative priors (SIDGPs). The successes, however, often hinge on appropriate early stopping (ES), which by far has largely been handled in an ad-hoc manner. In this paper, we propose the first principled method for ES when applying SIDGPs to IR, taking advantage of the typical bell trend of the reconstruction quality. In particular, our method is based on collaborative training and self-validation: the primal reconstruction process is monitored by a deep autoencoder, which is trained online with the historic reconstructed images and used to validate the reconstruction quality constantly. Experimentally, on several IR problems and different SIDGPs, our self-validation method is able to reliably detect near-peak performance and signal good ES points. Our code is available at https://sun-umn.github.io/Self-Validation/. IntroductionValidation-based ES is one of the most reliable strategies for controlling generalization errors in supervised learning, especially with potentially overspecified models such as in gradient boosting and modern DNNs [10,28,47]. Beyond supervised learning, ES often remains critical to learning success, but there are no principled ways-universal as validation for supervised learning-to decide when to stop. In this paper, we make the first step toward filling in the gap, and focus on solving IR, a central family of inverse problems, using training-free deep generative models.

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Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

Cited by 2 publications

References 13 publications

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors

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