Single-Layer Learnable Activation for Implicit Neural Representation
  (SL^{2}A-INR)
Implicit Neural Representation (INR), leveraging a neural network to
transform coordinate input into corresponding attributes, has recently driven
significant advances in several vision-related domains. However, the
performance of INR is heavily influenced by the choice of the nonlinear
activation function used in its multilayer perceptron (MLP) architecture.
Multiple nonlinearities have been investigated; yet, current INRs face
limitations in capturing high-frequency components, diverse signal types, and
handling inverse problems. We have identified that these problems can be
greatly alleviated by introducing a paradigm shift in INRs. We find that an
architecture with learnable activations in initial layers can represent fine
details in the underlying signals. Specifically, we propose SL^{2}A-INR, a
hybrid network for INR with a single-layer learnable activation function,
prompting the effectiveness of traditional ReLU-based MLPs. Our method performs
superior across diverse tasks, including image representation, 3D shape
reconstructions, inpainting, single image super-resolution, CT reconstruction,
and novel view synthesis. Through comprehensive experiments, SL^{2}A-INR sets
new benchmarks in accuracy, quality, and convergence rates for INR.
66ea3ec47196ffbb345c2109
Moein Heidari
admin_assigned
2024-09-18T08:23:24.402Z
61ba19bf6122a4fd29049371
https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
Moein Heidari
user
moein99
66ea3ec47196ffbb345c210a
Reza Rezaeian
admin_assigned
2024-09-18T08:36:58.297Z
6486bbd15111b6b2a88e3cce
/avatars/96da780df80980b5380e23afa1cfd69d.svg
Reza Rezaeian
user
Reza-R-77
66ea3ec47196ffbb345c210b
Reza Azad
66ea3ec47196ffbb345c210c
Dorit Merhof
66ea3ec47196ffbb345c210d
Hamid Soltanian-Zadeh
66ea3ec47196ffbb345c210e
Ilker Hacihaliloglu
Single-Layer Learnable Activation for Implicit Neural Representation
  (SL^{2}A-INR)
Implicit Neural Representation (INR), leveraging a neural network to
transform coordinate input into corresponding attributes, has recently driven
significant advances in several vision-related domains. However, the
performance of INR is heavily influenced by the choice of the nonlinear
activation function used in its multilayer perceptron (MLP) architecture.
Multiple nonlinearities have been investigated; yet, current INRs face
limitations in capturing high-frequency components, diverse signal types, and
handling inverse problems. We have identified that these problems can be
greatly alleviated by introducing a paradigm shift in INRs. We find that an
architecture with learnable activations in initial layers can represent fine
details in the underlying signals. Specifically, we propose SL^{2}A-INR, a
hybrid network for INR with a single-layer learnable activation function,
prompting the effectiveness of traditional ReLU-based MLPs. Our method performs
superior across diverse tasks, including image representation, 3D shape
reconstructions, inpainting, single image super-resolution, CT reconstruction,
and novel view synthesis. Through comprehensive experiments, SL^{2}A-INR sets
new benchmarks in accuracy, quality, and convergence rates for INR.
Implicit Neural Representation (INR) nonlinear activation function multilayer perceptron (MLP) high-frequency components diverse signal types inverse problems learnable activations SL$^{2}$A-INR hybrid network ReLU-based MLPs image representation 3D shape reconstructions inpainting single image super-resolution CT reconstruction novel view synthesis accuracy quality convergence rates