2022-10-19 更新
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Authors:Dmitrii Torbunov, Yi Huang, Haiwang Yu, Jin Huang, Shinjae Yoo, Meifeng Lin, Brett Viren, Yihui Ren
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https://github.com/LS4GAN/uvcgan.
PDF Accepted by WACV2023, contains 5 pages, 2 figures, 2 tables
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HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Authors:Aibek Alanov, Vadim Titov, Dmitry Vetrov
Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation technique that allows to optimize only 6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 to adapt to a target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally, we propose a new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to several domains depending on the input query. We propose the HyperDomainNet that is a hypernetwork that predicts our parameterization given the target domain. We empirically confirm that it can successfully learn a number of domains at once and may even generalize to unseen domains. Source code can be found at https://github.com/MACderRu/HyperDomainNet
PDF Accepted to NeurIPS 2022
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Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
Authors:Dong Chen, Xinda Qi, Yu Zheng, Yuzhen Lu, Zhaojian Li
Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are often cost-ineffective, environmentally unfriendly, or even posing a threat to food safety and human health. Recently, automated/robotic weeding using machine vision systems has seen increased research attention with its potential for precise and individualized weed treatment. However, dedicated, large-scale, and labeled weed image datasets are required to develop robust and effective weed identification systems but they are often difficult and expensive to obtain. To address this issue, data augmentation approaches, such as generative adversarial networks (GANs), have been explored to generate highly realistic images for agricultural applications. Yet, despite some progress, those approaches are often complicated to train or have difficulties preserving fine details in images. In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning. Comprehensive experimental results show that the developed approach consistently outperforms several state-of-the-art GAN models, representing the best trade-off between sample fidelity and diversity and highest FID score on a common weed dataset, CottonWeedID15. In addition, the expanding dataset with synthetic weed images can apparently boost model performance on four deep learning (DL) models for the weed classification tasks. Furthermore, the DL models trained on CottonWeedID15 dataset with only 10% of real images and 90% of synthetic weed images achieve a testing accuracy of over 94%, showing high-quality of the generated weed samples. The codes of this study are made publicly available at https://github.com/DongChen06/DMWeeds.
PDF 15 pages, 9 figures
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WaGI : Wavelet-based GAN Inversion for Preserving High-frequency Image Details
Authors:Seung-Jun Moon, Chaewon Kim, Gyeong-Moon Park
Recent GAN inversion models focus on preserving image-specific details through various methods, e.g., generator tuning or feature mixing. While those are helpful for preserving details compared to a naiive low-rate latent inversion, they still fail to maintain high-frequency features precisely. In this paper, we point out that the existing GAN inversion models have inherent limitations in both structural and training aspects, which preclude the delicate reconstruction of high-frequency features. Especially, we prove that the widely-used loss term in GAN inversion, i.e., L2, is biased to reconstruct low-frequency features mainly. To overcome this problem, we propose a novel GAN inversion model, coined WaGI, which enables to handle high-frequency features explicitly, by using a novel wavelet-based loss term and a newly proposed wavelet fusion scheme. To the best of our knowledge, WaGI is the first attempt to interpret GAN inversion in the frequency domain. We demonstrate that WaGI shows outstanding results on both inversion and editing, compared to the existing state-of-the-art GAN inversion models. Especially, WaGI robustly preserves high-frequency features of images even in the editing scenario. We will release our code with the pre-trained model after the review.
PDF
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