Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Organized Operational Neural Networks for Severe Image Restoration
  Problems   Discriminative learning based on convolutional neural networks (CNNs) aims to
perform image restoration by learning from training examples of noisy-clean
image pairs. It has become the go-to methodology for tackling image restoration
and has outperformed the traditional non-local class of methods. However, the
top-performing networks are generally composed of many convolutional layers and
hundreds of neurons, with trainable parameters in excess of several millions.
We claim that this is due to the inherent linear nature of convolution-based
transformation, which is inadequate for handling severe restoration problems.
Recently, a non-linear generalization of CNNs, called the operational neural
networks (ONN), has been shown to outperform CNN on AWGN denoising. However,
its formulation is burdened by a fixed collection of well-known nonlinear
operators and an exhaustive search to find the best possible configuration for
a given architecture, whose efficacy is further limited by a fixed output layer
operator assignment. In this study, we leverage the Taylor series-based
function approximation to propose a self-organizing variant of ONNs, Self-ONNs,
for image restoration, which synthesizes novel nodal transformations onthe-fly
as part of the learning process, thus eliminating the need for redundant
training runs for operator search. In addition, it enables a finer level of
operator heterogeneity by diversifying individual connections of the receptive
fields and weights. We perform a series of extensive ablation experiments
across three severe image restoration tasks. Even when a strict equivalence of
learnable parameters is imposed, Self-ONNs surpass CNNs by a considerable
margin across all problems, improving the generalization performance by up to 3
dB in terms of PSNR.

4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas