Generative adversarial networks (GAN) rely on the adversarial interplay between a generator (G) and a discriminator (D), where G aims to synthesize realistic data while D learns to distinguish real from generated samples. However, this dynamic often suffers from imbalance, as D quickly becomes proficient at classification, leading to overfitting and limiting G’s ability to generate diverse, high-quality outputs especially in data-limited scenarios. Existing methods primarily focus on improving D, inadvertently exacerbating this imbalance by making G’s task even more challenging. In this paper, we introduce DGA-GAN, a novel training paradigm that dynamically reshapes G’s strategy throughout training, allowing it to generate more diverse and high-fidelity samples while maintaining adversarial balance. Our approach introduces an expansion-consolidation mechanism, where G iteratively broadens its generative scope by learning from varied data subsets and consolidates this knowledge to refine its overall strategy. This dynamic adaptation forces D to continuously evolve rather than overfit, leading to a more stable and effective adversarial game. We demonstrate that DGA-GAN improves sample quality and diversity across multiple datasets (CIFAR-10, TinyImageNet, few-shot generation benchmarks) and GAN architectures (SNGAN, BigGAN, StyleGAN2, GAN-LTH, Re-GAN). Furthermore, our method seamlessly integrates with existing data augmentation techniques to enhance performance in data-limited settings. By introducing a generative model that adapts its learning trajectory in response to D’s evolution, DGA-GAN represents a paradigm shift in GAN training, paving the way for more adaptive and data-efficient generative models.

Recent advances in deep learning have witnessed many successful medical image translation models that learn correspondences between two visual domains. However, building robust mappings between domains is a significant challenge when handling misalignments caused by factors such as respiratory motion and anatomical changes. This issue is further exacerbated in scenarios with limited data availability, leading to a significant degradation in translation quality. In this paper, we introduce a novel data-efficient framework for aligning medical images via Reconfigurable Generative Network (Reconfig-MIT) for high-quality image translation. The key idea of Reconfig-MIT is to adaptively expand the generative network width within a Generative Adversarial Networks (GAN) architecture, initially expanding rapidly to capture low-level features and then slowing to refine high-level complexities. This dynamic network adaptation mechanism allows to adaptively learn at different rates, thus the model can better respond to deviations in the data caused by misalignments, while maintaining an effective equilibrium with the discriminator (D). We also introduce the Recursive Cycle-Consistency Loss (R-CCL), which extends the cycle consistency loss to effectively preserve key anatomical structures and their spatial relationships, improving translation quality. Extensive experiments show that Reconfig-MIT is a generic framework that enables easy integration with existing image translation methods, including those incorporating registration networks used for correcting misalignments, and provides robust and high-quality translation on paired and unpaired misaligned data in both data-rich and data-limited scenarios. https://github.com/IntellicentAI-Lab/Reconfig-MIT.

The training of Generative Adversarial Networks (GANs) for high-fidelity images has predominantly relied on large-scale datasets. Emerging research, particularly on GANs 'lottery tickets', suggests that dense GANs models have sparse sub-networks capable of superior performance with limited data. However, the conventional process to uncover these 'lottery tickets' involves a resource-intensive train-prune-retrain cycle. Addressing this, our paper introduces Re-GAN, a novel, data-efficient approach for GANs training that dynamically reconfigures the GANs architecture during training. This method focuses on iterative pruning of non-important connections and regrowing them, thereby preventing premature loss of important features and maintaining the model's representational strength. Re-GAN provides a more stable and efficient solution for GANs models with limited data, offering an alternative to existing progressive growing methods and GANs tickets. While Re-GAN has already demonstrated its potential in image generation across diverse datasets, domains, and resolutions, in this paper, we significantly expand our study. We incorporate new applications, notably Image-to-Image translation, include additional datasets, provide in-depth analyses, and explore compatibility with data augmentation techniques. This expansion not only broadens the scope of Re-GAN but also establishes it as a generic training methodology, demonstrating its effectiveness and adaptability in different GANs scenarios.

Training Generative Adversarial Networks (GAN) to generate high-quality images typically requires large datasets. Network pruning during training has recently emerged as a significant advancement for data-efficient GAN. However, simple and straightforward pruning can lead to the risk of losing key information, resulting in suboptimal results due to GAN’s competitive dynamics between generator (G) and discriminator (D). Addressing this, we present RG-GAN, a novel approach that marks the first incorporation of dynamic weight regeneration and pruning in GAN training to improve the quality of the generated samples, even with limited data. Specifically, RG-GAN initiates layer-wise dynamic pruning by removing less important weights to the quality of the generated images. While pruning enhances efficiency, excessive sparsity within layers can pose a risk of model collapse. To mitigate this issue, RG-GAN applies a dynamic regeneration method to reintroduce specific weights when they become important, ensuring a balance between sparsity and image quality. Though effective, the sparse network achieved through this process might eliminate some weights important to the combined G and D performance, a crucial aspect for achieving stable and effective GAN training. RG-GAN addresses this loss of weights by integrating learned sparse network weights back into the dense network at the previous stage during a follow-up regeneration step. Our results consistently demonstrate RG-GAN’s robust performance across a variety of scenarios, including different GAN architectures, datasets, and degrees of data scarcity, reinforcing its value as a generic training methodology. Results also show that data augmentation exhibits improved performance in conjunction with RG-GAN. Furthermore, RG-GAN can achieve fewer parameters without compromising, and even enhancing, the quality of the generated samples. Code can be found at this link: https://github.com/IntellicentAI-Lab/RG-GAN

Training Generative Adversarial Networks (GANs) on high-fidelity images usually requires a vast number of training images. Recent research on GAN tickets reveals that dense GANs models contain sparse sub-networks or “lottery tickets” that, when trained separately, yield better results under limited data. However, finding GANs tickets requires an expensive process of train-prune-retrain. In this paper, we propose Re-GAN, a data-efficient GANs training that dynamically reconfigures GANs architecture during training to explore different sub-network structures in training time. Our method repeatedly prunes unimportant connections to regularize GANs network and regrows them to reduce the risk of prematurely pruning important connections. Re-GAN stabilizes the GANs models with less data and offers an alternative to the existing GANs tickets and progressive growing methods. We demonstrate that Re-GAN is a generic training methodology which achieves stability on datasets of varying sizes, domains, and resolutions (CIFAR-10, Tiny-ImageNet, and multiple few-shot generation datasets) as well as different GANs architectures (SNGAN, ProGAN, StyleGAN2 and AutoGAN). Re-GAN also improves performance when combined with the recent augmentation approaches. Moreover, Re-GAN requires fewer floating-point operations (FLOPs) and less training time by removing the unimportant connections during GANs training while maintaining comparable or even generating higher-quality samples. When compared to state-of-the-art StyleGAN2, our method outperforms without requiring any additional fine-tuning step. Code can be found at this link: https://github.com/IntellicentAI-Lab/Re-GAN

Unsupervised image-to-image translation aims to learn the mapping from an input image in a source domain to an output image in a target domain without paired training dataset. Recently, remarkable progress has been made in translation due to the development of generative adversarial networks (GANs). However, existing methods suffer from the training instability as gradients passing from discriminator to generator become less informative when the source and target domains exhibit sufficiently large discrepancies in appearance or shape. To handle this challenging problem, in this paper, we propose a novel multi-constraint adversarial model (MCGAN) for image translation in which multiple adversarial constraints are applied at generator's multi-scale outputs by a single discriminator to pass gradients to all the scales simultaneously and assist generator training for capturing large discrepancies in appearance between two domains. We further notice that the solution to regularize generator is helpful in stabilizing adversarial training, but results may have unreasonable structure or blurriness due to less context information flow from discriminator to generator. Therefore, we adopt dense combinations of the dilated convolutions at discriminator for supporting more information flow to generator. With extensive experiments on three public datasets, cat-to-dog, horse-to-zebra, and apple-to-orange, our method significantly improves state-of-the-arts on all datasets.

Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals' wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users' devices probe requests and locations. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3 percent.