Papers › Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and...

Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition

23 Oct 2024IEEE Access 2024 10archive 2025-07-28

Prasanna Reddy Pulakurthi, Mahsa Mozaffari, Sohail Dianat, Jamison Heard, Raghuveer Rao, Majid Rabbani

Generative Adversarial Networks (GANs) have gained considerable attention owing to their impressive ability to generate high-quality, realistic images from a desired data distribution. This research introduces advancements in GANs by developing an improved activation function, a novel training strategy, and an adaptive rank decomposition method to compress the network. The proposed activation function, called Parametric Mish (PMish), automatically adjusts a trainable parameter to control the smoothness and shape of the activation function. Our method employs a Neural Architecture Search (NAS) to discover the optimal architecture for image generation while using the Maximum Mean Discrepancy (MMD) repulsive loss for adversarial training. The proposed novel training strategy improves performance by progressively increasing the upper bound of the bounded MMD-GAN repulsive loss. Finally, the proposed Adaptive Rank Decomposition (ARD) method reduces the complexity of the network with minimal impact on its generative performance, thus enabling efficient deployment on resource-limited platforms. The effectiveness of these advancements is rigorously tested on standard benchmark datasets such as CIFAR-10, CIFAR-100, STL-10, and CelebA, where significant improvements over existing techniques are demonstrated. The implementation code is available at: https://github.com/PrasannaPulakurthi/MMD-PMish-NAS

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Results from the paper archive 2025-07-28

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: PMish

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections