Unsupervised Training of Convex Neural Network Regularizers using Maximum Likelihood Estimation

Core Concepts

An unsupervised Bayesian training approach is proposed to learn convex neural network regularizers using only a fixed noisy dataset, based on a dual Markov chain estimation method. The learned regularizers demonstrate close performance to supervised adversarial regularization methods on natural image Gaussian deconvolution and Poisson denoising tasks.

Abstract

The content discusses an unsupervised Bayesian training approach for learning convex neural network regularizers to solve inverse imaging problems, such as image denoising and deconvolution.
Key highlights:

Inverse imaging problems are often ill-posed, requiring the use of regularized reconstruction operators. Variational regularization combines a data fidelity term with priors like wavelet or total variation priors.
The authors propose an unsupervised Bayesian training approach to learn convex neural network regularizers using only a fixed noisy dataset, without requiring clean ground truth data.
The approach is based on a dual Markov chain estimation method that jointly maximizes the prior and posterior distributions.
Experiments on Gaussian deconvolution and Poisson denoising tasks show the learned unsupervised regularizers perform close to supervised adversarial regularization methods.
The unsupervised regularizers also demonstrate better generalization properties compared to end-to-end deep learning methods when transferred to a different forward operator.
The authors extend previous work on Bayesian estimation of regularization parameters to the more general case of learning a convex neural network regularizer with a significantly larger number of parameters. The proposed algorithm and convergence analysis provide a framework for unsupervised training of image priors in high-dimensional inverse problems.

Stats

The initial PSNR of the corrupted image is 22.38dB for Gaussian deconvolution and 21.16dB for Poisson denoising.

Quotes

"Unsupervised learning is a training approach in the situation where ground truth data is unavailable, such as inverse imaging problems."
"Variational models combine a data fidelity term with priors such as wavelet priors or total variation priors to define the reconstruction of a measurement."
"The variational formulation can be formulated as a special case of the Bayesian formulation, where the negative prior log-density and negative log-likelihood correspond to the regularization and fidelity terms respectively."

Key Insights Distilled From

Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation

by Hong... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05445.pdf

Deeper Inquiries

How can the proposed unsupervised training framework be extended to learn more expressive, non-convex regularizers while maintaining theoretical guarantees

The proposed unsupervised training framework can be extended to learn more expressive, non-convex regularizers while maintaining theoretical guarantees by incorporating techniques such as non-convex optimization methods and adaptive sampling strategies. One approach could involve utilizing stochastic optimization algorithms tailored for non-convex functions, such as stochastic gradient descent with restarts or adaptive learning rates. By introducing non-convexity into the regularizer parameterization, the model can capture more complex patterns and structures in the data, leading to improved performance in challenging inverse problems. Additionally, incorporating regularization techniques that promote sparsity or structured sparsity can further enhance the expressiveness of the learned regularizers.

What are the limitations of the current Markov chain sampling approach, and how could it be improved to handle more challenging inverse problems or larger-scale datasets

The current Markov chain sampling approach has limitations in handling more challenging inverse problems or larger-scale datasets due to issues such as slow convergence, high computational complexity, and sensitivity to hyperparameters. To address these limitations, several improvements can be made:

Advanced Sampling Techniques: Implementing advanced sampling techniques like Hamiltonian Monte Carlo or stochastic gradient Langevin dynamics can improve the exploration of the parameter space and accelerate convergence.
Parallelization: Utilizing parallel computing resources to run multiple Markov chains simultaneously can speed up the sampling process and handle larger datasets more efficiently.
Adaptive Step Sizes: Incorporating adaptive step sizes for the Markov chain updates can enhance the exploration of the parameter space and improve convergence rates.
Mini-Batching: Implementing mini-batching techniques to process data in smaller subsets can reduce memory requirements and computational load, making it more scalable for larger datasets.
Regularization: Introducing regularization techniques to stabilize the training process and prevent overfitting can improve the robustness of the model and its generalization capabilities.

Can the unsupervised learning of image priors be combined with other self-supervised or meta-learning techniques to further improve the generalization and transfer learning capabilities

The unsupervised learning of image priors can be combined with other self-supervised or meta-learning techniques to further improve generalization and transfer learning capabilities. By integrating self-supervised learning methods like contrastive learning or rotation prediction tasks, the model can learn more robust and invariant representations of the data, enhancing its ability to generalize to unseen scenarios. Meta-learning approaches can be employed to adapt the learned image priors to new tasks or datasets efficiently, enabling faster adaptation and improved performance on diverse inverse problems. Additionally, techniques like domain adaptation and few-shot learning can be leveraged to transfer knowledge from related tasks to enhance the performance of the unsupervised learned image priors on new domains or datasets. By integrating these complementary techniques, the model can achieve better generalization, robustness, and transfer learning capabilities across a wide range of imaging tasks.