msa-inr 0.1.1

Multi-Scale Sine Activation for Implicit Neural Representations

MSA-INR

MSA-INR is a lightweight PyTorch package for using Multi-Scale Sine Activation (MSA) in implicit neural representation (INR) models.

This package provides a simple implementation of the activation function proposed in our AAAI 2026 paper:

Implicit Neural Representation with Multi-Scale Sine Activation
Jufeng Han, Shu Wei, Min Wu, Lina Yu, Weijun Li, Linjun Sun, Hong Qin, and Yan Pang
Proceedings of the AAAI Conference on Artificial Intelligence, 40(26): 21567--21575, 2026.

Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/39305

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Introduction

Implicit Neural Representations (INRs) model continuous signals using neural networks that map coordinates to signal values. They have been widely used in image representation, video representation, 3D shape modeling, neural fields, and scientific computing.

However, standard multilayer perceptrons often suffer from spectral bias, where low-frequency components are learned more easily than high-frequency details. This limitation makes it difficult for conventional INRs to accurately represent signals with fine structures and multi-scale patterns.

To address this problem, we propose Multi-Scale Sine Activation (MSA). MSA introduces logarithmically spaced multi-scale sinusoidal transformations into the activation function, allowing different hidden channels to respond to different frequency scales. This design improves the spectral expressivity of INR models while keeping the network structure simple and efficient.

In the AAAI 2026 paper, MSA is evaluated on multiple implicit representation tasks, including 1D multi-scale function fitting, image representation, video representation, 3D shape representation, and PDE solving.

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Installation

Install from PyPI:

pip install msa-inr

Upgrade to the latest version:

pip install -U msa-inr

Or install from source:

git clone https://github.com/your-username/MSA_INR.git
cd MSA_INR
pip install -e .

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Quick Start

Use MSA as an activation function

import torch
from msa_inr import MSA

activation = MSA(s_min=1.0, s_max=5.0)

x = torch.randn(8, 256)
y = activation(x)

print(y.shape)

For backward compatibility, SineActivation is also available as an alias of MSA:

from msa_inr import SineActivation

activation = SineActivation(s_min=1.0, s_max=5.0)

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Use MSA-INR network

import torch
from msa_inr import MSANet

model = MSANet(
    in_features=2,
    hidden_features=256,
    hidden_layers=3,
    out_features=1,
    s_min=[1.0, 1.0, 1.0],
    s_max=[5.0, 5.0, 5.0],
)

coords = torch.rand(1024, 2)
pred = model(coords)

print(pred.shape)

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Use get_model

from msa_inr import get_model

model = get_model(
    in_features=3,
    hidden_features=256,
    hidden_layers=2,
    out_features=1,
)

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Method Overview

Given an input tensor with feature dimension C, MSA constructs a set of log-spaced frequencies:

freqs = logspace(s_min, s_max, steps=C)

and applies a channel-wise sinusoidal transformation:

MSA(x) = sin(freqs * x)

In this way, different hidden channels are associated with different frequency responses. Low-frequency channels help capture smooth structures, while high-frequency channels improve the representation of fine details and rapidly varying components.

This makes MSA suitable for implicit neural representation tasks where both global structure and local high-frequency details are important.

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API Reference

MSA

MSA(s_min=1.0, s_max=5.0)

Multi-scale sine activation with log-spaced frequencies.

Parameters

Parameter	Type	Default	Description
s_min	float	1.0	Lower exponent of the log-spaced frequency range
s_max	float	5.0	Upper exponent of the log-spaced frequency range

Example

import torch
from msa_inr import MSA

act = MSA(s_min=1.0, s_max=5.0)

x = torch.randn(16, 128)
y = act(x)

print(y.shape)

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MSANet

MSANet(
    in_features=3,
    hidden_features=256,
    hidden_layers=2,
    out_features=1,
    s_min=None,
    s_max=None,
)

A simple INR network using MSA activation layers.

Parameters

Parameter	Type	Default	Description
in_features	int	3	Input coordinate dimension
hidden_features	int	256	Hidden layer width
hidden_layers	int	2	Number of hidden MSA layers
out_features	int	1	Output dimension
s_min	float, list, or None	None	Lower frequency exponents for each hidden layer
s_max	float, list, or None	None	Upper frequency exponents for each hidden layer

If s_min and s_max are not specified, the default setting is:

s_min = [1.0] * hidden_layers
s_max = [5.0] * hidden_layers

A scalar value is also supported:

model = MSANet(hidden_layers=3, s_min=1.0, s_max=5.0)

which is equivalent to:

model = MSANet(
    hidden_layers=3,
    s_min=[1.0, 1.0, 1.0],
    s_max=[5.0, 5.0, 5.0],
)

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Example: Fitting a 2D Signal

import torch
import torch.nn as nn
from msa_inr import MSANet

# Coordinate input: [N, 2]
coords = torch.rand(4096, 2)

# Target signal
target = torch.sin(20 * coords[:, :1]) * torch.cos(20 * coords[:, 1:2])

model = MSANet(
    in_features=2,
    hidden_features=256,
    hidden_layers=3,
    out_features=1,
    s_min=[1.0, 1.0, 1.0],
    s_max=[5.0, 5.0, 5.0],
)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.MSELoss()

for step in range(1000):
    pred = model(coords)
    loss = criterion(pred, target)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if step % 100 == 0:
        print(f"Step {step}, Loss: {loss.item():.6f}")

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Project Structure

MSA_INR/
├── pyproject.toml
├── README.md
├── msa.png
├── LICENSE
├── src/
│   └── msa_inr/
│       ├── __init__.py
│       ├── activations.py
│       └── models.py
├── examples/
│   ├── use_activation.py
│   └── use_model.py
└── tests/
    └── test_basic.py

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Build from Source

Install build tools:

pip install build twine

Build the package:

python -m build

Check the package:

twine check dist/*

Upload to PyPI:

twine upload dist/*

After uploading, users can install it using:

pip install msa-inr

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Citation

If you use this package or the MSA activation function in your research, please cite our paper:

@inproceedings{han2026implicit,
  title={Implicit Neural Representation with Multi-Scale Sine Activation},
  author={Han, Jufeng and Wei, Shu and Wu, Min and Yu, Lina and Li, Weijun and Sun, Linjun and Qin, Hong and Pang, Yan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={26},
  pages={21567--21575},
  year={2026},
  doi={10.1609/aaai.v40i26.39305}
}

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License

This project is released under the MIT License.

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Contact

For questions, issues, or suggestions, please open an issue in the GitHub repository.