Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond

• URL: http://arxiv.org/abs/2010.07069v2
• Date: Tue, 20 Oct 2020 14:44:52 GMT
• Title: Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond
• Authors: Rajaei Khatib, Dror Simon and Michael Elad
• Abstract summary: We propose an unfolded version of a greedy pursuit algorithm for the same goal. Key features of our Learned Greedy Method (LGM) are the ability to accommodate a dynamic number of unfolded layers.
• Score: 24.160276545294288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fields of signal and image processing have been deeply influenced by the introduction of deep neural networks. These are successfully deployed in a wide range of real-world applications, obtaining state of the art results and surpassing well-known and well-established classical methods. Despite their impressive success, the architectures used in many of these neural networks come with no clear justification. As such, these are usually treated as "black box" machines that lack any kind of interpretability. A constructive remedy to this drawback is a systematic design of such networks by unfolding well-understood iterative algorithms. A popular representative of this approach is the Iterative Shrinkage-Thresholding Algorithm (ISTA) and its learned version -- LISTA, aiming for the sparse representations of the processed signals. In this paper we revisit this sparse coding task and propose an unfolded version of a greedy pursuit algorithm for the same goal. More specifically, we concentrate on the well-known Orthogonal-Matching-Pursuit (OMP) algorithm, and introduce its unfolded and learned version. Key features of our Learned Greedy Method (LGM) are the ability to accommodate a dynamic number of unfolded layers, and a stopping mechanism based on representation error, both adapted to the input. We develop several variants of the proposed LGM architecture and test some of them in various experiments, demonstrating their flexibility and efficiency.

Related papers

• Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods [6.26650196870495]
  Convolutional Neural Networks (CNNs) have been able to assist humans in solving many real-world problems. This paper proposes a stacking scheme based on the linear multi-step method to enhance the performance of CNNs.
  arXiv  Detail & Related papers  (2024-09-08T05:13:58Z)
• Training Neural Networks with Internal State, Unconstrained Connectivity, and Discrete Activations [66.53734987585244]
  True intelligence may require the ability of a machine learning model to manage internal state. We show that we have not yet discovered the most effective algorithms for training such models. We present one attempt to design such a training algorithm, applied to an architecture with binary activations and only a single matrix of weights.
  arXiv  Detail & Related papers  (2023-12-22T01:19:08Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [62.691996239590125]
  We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction. Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image. Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
  arXiv  Detail & Related papers  (2022-05-16T06:49:36Z)
• SIRe-Networks: Skip Connections over Interlaced Multi-Task Learning and Residual Connections for Structure Preserving Object Classification [28.02302915971059]
  In this paper, we introduce an interlaced multi-task learning strategy, defined SIRe, to reduce the vanishing gradient in relation to the object classification task. The presented methodology directly improves a convolutional neural network (CNN) by enforcing the input image structure preservation through auto-encoders. To validate the presented methodology, a simple CNN and various implementations of famous networks are extended via the SIRe strategy and extensively tested on the CIFAR100 dataset.
  arXiv  Detail & Related papers  (2021-10-06T13:54:49Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Understanding Neural Code Intelligence Through Program Simplification [3.9704927572880253]
  We propose a model-agnostic approach to identify critical input features for models in code intelligence systems. Our approach, SIVAND, uses simplification techniques that reduce the size of input programs of a CI model. We believe that SIVAND's extracted features may help understand neural CI systems' predictions and learned behavior.
  arXiv  Detail & Related papers  (2021-06-07T05:44:29Z)
• Towards Sample Efficient Agents through Algorithmic Alignment [25.23741737716188]
  We propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming.
  arXiv  Detail & Related papers  (2020-08-07T15:44:32Z)
• MetaSDF: Meta-learning Signed Distance Functions [85.81290552559817]
  Generalizing across shapes with neural implicit representations amounts to learning priors over the respective function space. We formalize learning of a shape space as a meta-learning problem and leverage gradient-based meta-learning algorithms to solve this task.
  arXiv  Detail & Related papers  (2020-06-17T05:14:53Z)
• Lossless Compression of Deep Neural Networks [17.753357839478575]
  Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition. It is challenging to deploy these networks under limited computational resources, such as in mobile devices. We introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced.
  arXiv  Detail & Related papers  (2020-01-01T15:04:43Z)

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.