Smooth Deep Image Generator from Noises

Guo, Tianyu; Xu, Chang; Shi, Boxin; Xu, Chao; Tao, Dacheng

doi:10.1609/aaai.v33i01.33013731

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…A decoder-encoder network was particularly designed to map the random noise vectors to informative ones and feed them to the generator of the adversarial networks [2]. A smooth generator was proposed by manipulating the output noise to cope with the perturbation during sample generation [3]. Therefore, we believe that noise manipulation provides a promising potential to produce highfidelity sample generation in GANs.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…Different from the methods aiming at enhancing the discrimination of D, a few of the new mechanisms have been revealed to manipulate the output noise z [2,3]. Here, we are going to take advantage of the frequency decomposed by the wavelet transformations.…”

Section: Gan Architecturesmentioning

confidence: 99%

“…Noise sampling, started from random initialization most of the time, plays an intrinsic role in the two-player game of Generative Adversarial Networks (GANs) [1], where the generator G is designed to capture the data distribution as accurately as possible, through the adversarial training by the discriminator D. The only way to generate fake samples with adequate fidelity, with the help of the sampled noise, used to depend on the fine tuning process after the discrimination by D. However, this dependency of tough training after randomness leads to unsatisfactory sample generation from time to time. of GANs, manipulating the noise sampling [2,3] showed a promising result and therefore gets a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

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Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

Zeng

Zhang²

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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In a typical Generative Adversarial Network (GAN), a noise is sampled to generate fake samples via a series of convolutional operations after random initialization. However, current GANs merely rely on the pixel space to sample the noise, increasing the difficulty of approaching the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. The proposed WaveletNet embeds a wavelet deconvolution layer in the generator to take advantage of the wavelet deconvolution. By learning a filter with multiple channels, or multiple convolutional filters, it can efficiently homogenize the sampled noise via an averaging operation, to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST, and SVHN datasets through an open GAN benchmark tool. The results showed that WaveletGAN has excellent performance in generating high-fidelity samples.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Section: Gan Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

Zeng

Zhang²

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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show abstract

“…A decoder-encoder network was particularly designed to map the random noise vectors to informative ones and feed them to the generator of the adversarial networks [25]. A smooth generator was proposed by manipulating the output noise to cope with the perturbation during sample generation [26]. Therefore, we believe that noise manipulation provides a promising potential to produce high-fidelity sample generation in GANs.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…Different from the methods aiming at enhancing the discrimination of D, a few of the new mechanisms have been revealed to manipulate the output noise z [24,25,26]. Here, we are going to take advantage of the frequency decomposed by the wavelet transformations.…”

Section: Gan Architecturesmentioning

confidence: 99%

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs

Zeng

Zhang²

2020

Preprint

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In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations. However, current noise generation models merely relies on the information from the pixel space, which increases the difficulty to approach the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. When embedding a wavelet deconvolution layer in the generator, the resultant GAN, called WaveletGAN, takes advantage of the wavelet deconvolution to learn a filtering with multiple channels, which can efficiently homogenize the generated noise via an averaging operation, so as to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST and SVHN datasets through an open GAN benchmark tool. The results show that WaveletGAN has excellent performance in generating high-fidelity samples, thanks to the smallest FIDs obtained on these datasets.

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LncRTPred: Predicting RNA–RNA mode of interaction mediated by lncRNA

Das,

Parida

et al. 2023

IUBMB Life

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Long non‐coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA–mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real‐time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML‐based lncRNA–mRNA target prediction model‐ ‘LncRTPred’. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non‐interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease‐specific lncRNA–mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA–mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

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Smooth Deep Image Generator from Noises

Cited by 5 publications

References 8 publications

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs

LncRTPred: Predicting RNA–RNA mode of interaction mediated by lncRNA

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