L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks

• URL: http://arxiv.org/abs/2311.05548v1
• Date: Thu, 9 Nov 2023 17:47:32 GMT
• Title: L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks
• Authors: Mirat Shah, Vansh Jain, Anmol Chokshi, Guruprasad Parasnis, Pramod Bide
• Abstract summary: This paper introduces L-WaveBlock, a novel and robust feature extractor that leverages the capabilities of the Discrete Wavelet Transform (DWT) with deep learning methodologies. L-WaveBlock is catered to quicken the convergence of GAN generators while simultaneously enhancing their performance. The paper demonstrates the remarkable utility of L-WaveBlock across three datasets, a road satellite imagery dataset, the CelebA dataset and the GoPro dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Adversarial Networks (GANs) have risen to prominence in the field of deep learning, facilitating the generation of realistic data from random noise. The effectiveness of GANs often depends on the quality of feature extraction, a critical aspect of their architecture. This paper introduces L-WaveBlock, a novel and robust feature extractor that leverages the capabilities of the Discrete Wavelet Transform (DWT) with deep learning methodologies. L-WaveBlock is catered to quicken the convergence of GAN generators while simultaneously enhancing their performance. The paper demonstrates the remarkable utility of L-WaveBlock across three datasets, a road satellite imagery dataset, the CelebA dataset and the GoPro dataset, showcasing its ability to ease feature extraction and make it more efficient. By utilizing DWT, L-WaveBlock efficiently captures the intricate details of both structural and textural details, and further partitions feature maps into orthogonal subbands across multiple scales while preserving essential information at the same time. Not only does it lead to faster convergence, but also gives competent results on every dataset by employing the L-WaveBlock. The proposed method achieves an Inception Score of 3.6959 and a Structural Similarity Index of 0.4261 on the maps dataset, a Peak Signal-to-Noise Ratio of 29.05 and a Structural Similarity Index of 0.874 on the CelebA dataset. The proposed method performs competently to the state-of-the-art for the image denoising dataset, albeit not better, but still leads to faster convergence than conventional methods. With this, L-WaveBlock emerges as a robust and efficient tool for enhancing GAN-based image generation, demonstrating superior convergence speed and competitive performance across multiple datasets for image resolution, image generation and image denoising.

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