Riemann Noise Injection With PyTorch

Last update: May 27, 2022

Related tags

Overview

Riemann Noise Injection - PyTorch

A module for modeling GAN noise injection based on Riemann geometry, as described in Ruili Feng, Deli Zhao, and Zheng-Jun Zha's paper "Understanding Noise Injection in GANs".

import torch
from riemann_noise_pytorch import RiemannNoise

class Generator(torch.nn.Module):
    def __init__(self):
        ...
        self.riemann_noise = RiemannNoise(128, torch.device("cuda"))
        ...
    def forward(self, x):
        out = self.DownBlock(x)
        out = self.resblock(out)
        out = self.riemann_noise(out)
        out = self.UpBlock(out)
        return out

Citations

@InProceedings{pmlr-v139-feng21g,
  title = 	 {Understanding Noise Injection in GANs},
  author =       {Feng, Ruili and Zhao, Deli and Zha, Zheng-Jun},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {3284--3293},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/feng21g/feng21g.pdf},
  url = 	 {https://proceedings.mlr.press/v139/feng21g.html},
  abstract = 	 {Noise injection is an effective way of circumventing overfitting and enhancing generalization in machine learning, the rationale of which has been validated in deep learning as well. Recently, noise injection exhibits surprising effectiveness when generating high-fidelity images in Generative Adversarial Networks (GANs) (e.g. StyleGAN). Despite its successful applications in GANs, the mechanism of its validity is still unclear. In this paper, we propose a geometric framework to theoretically analyze the role of noise injection in GANs. First, we point out the existence of the adversarial dimension trap inherent in GANs, which leads to the difficulty of learning a proper generator. Second, we successfully model the noise injection framework with exponential maps based on Riemannian geometry. Guided by our theories, we propose a general geometric realization for noise injection. Under our novel framework, the simple noise injection used in StyleGAN reduces to the Euclidean case. The goal of our work is to make theoretical steps towards understanding the underlying mechanism of state-of-the-art GAN algorithms. Experiments on image generation and GAN inversion validate our theory in practice.}
}

@article{Karras2019stylegan2,
  title   = {Analyzing and Improving the Image Quality of {StyleGAN}},
  author  = {Tero Karras and Samuli Laine and Miika Aittala and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  journal = {CoRR},
  volume  = {abs/1912.04958},
  year    = {2019},
}

Riemann Noise Injection With PyTorch

Related tags

Overview

Riemann Noise Injection - PyTorch

Citations

Owner

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Phy-Q: A Benchmark for Physical Reasoning

Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Repo for parser tensorflow(.pb) and tflite(.tflite)

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Histocartography is a framework bringing together AI and Digital Pathology

code for "Feature Importance-aware Transferable Adversarial Attacks"

Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

Learning Correspondence from the Cycle-consistency of Time (CVPR 2019)

This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

Match SafeGraph POIs with Data collected through a cultural resource survey in Washington DC.

A python implementation of Deep-Image-Analogy based on pytorch.