HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization

• URL: http://arxiv.org/abs/2503.19074v1
• Date: Mon, 24 Mar 2025 19:00:28 GMT
• Title: HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization
• Authors: Osman Goni, Himadri Saha Arka, Mithun Halder, Mir Moynuddin Ahmed Shibly, Swakkhar Shatabda,
• Abstract summary: We introduce HingeRLC-GAN, a novel approach that combines RLC Regularization and the Hinge loss function.<n>With a FID Score of 18 and a KID Score of 0.001, our approach outperforms existing methods by effectively balancing training stability and increased diversity.
• Score: 1.2058600649065618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Generative Adversarial Networks (GANs) have demonstrated their capability for producing high-quality images. However, a significant challenge remains mode collapse, which occurs when the generator produces a limited number of data patterns that do not reflect the diversity of the training dataset. This study addresses this issue by proposing a number of architectural changes aimed at increasing the diversity and stability of GAN models. We start by improving the loss function with Wasserstein loss and Gradient Penalty to better capture the full range of data variations. We also investigate various network architectures and conclude that ResNet significantly contributes to increased diversity. Building on these findings, we introduce HingeRLC-GAN, a novel approach that combines RLC Regularization and the Hinge loss function. With a FID Score of 18 and a KID Score of 0.001, our approach outperforms existing methods by effectively balancing training stability and increased diversity.

Related papers

• Improving $(α, f)$-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance [7.8973037023478785]
  Federated Learning (FL) is a potential solution to data privacy challenges in distributed machine learning. FL systems remain vulnerable to Byzantine attacks, where malicious nodes contribute corrupted model updates. This paper introduces Layerwise Cosine Aggregation, a novel aggregation scheme designed to enhance robustness of these rules in high-dimensional settings.
  arXiv  Detail & Related papers  (2025-03-27T08:07:39Z)
• ROCM: RLHF on consistency models [8.905375742101707]
  We propose a reward optimization framework for applying RLHF to consistency models. We investigate various $f$-divergences as regularization strategies, striking a balance between reward and model consistency.
  arXiv  Detail & Related papers  (2025-03-08T11:19:48Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data [16.574346252357653]
  We propose a novel regularization method based on the idea of renormalization group (RG) in physics. We show that our method can effectively enhance the performance and stability of GANs under limited data scenarios.
  arXiv  Detail & Related papers  (2024-08-20T18:37:37Z)
• DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints [68.82294911302579]
  We introduce DiveR-CT, which relaxes conventional constraints on the objective and semantic reward, granting greater freedom for the policy to enhance diversity. Our experiments demonstrate DiveR-CT's marked superiority over baselines by 1) generating data that perform better in various diversity metrics across different attack success rate levels, 2) better-enhancing resiliency in blue team models through safety tuning based on collected data, 3) allowing dynamic control of objective weights for reliable and controllable attack success rates, and 4) reducing susceptibility to reward overoptimization.
  arXiv  Detail & Related papers  (2024-05-29T12:12:09Z)
• MCGAN: Enhancing GAN Training with Regression-Based Generator Loss [5.7645234295847345]
  Generative adversarial networks (GANs) have emerged as a powerful tool for generating high-fidelity data.<n>Main bottleneck of existing approaches is the lack of supervision on the generator training.<n>We propose an algorithm called Monte Carlo GAN (MCGAN)
  arXiv  Detail & Related papers  (2024-05-27T14:15:52Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution [88.13972071356422]
  We propose a diffusion-style data augmentation scheme for GAN-based image super-resolution (SR) methods, known as DifAugGAN. It involves adapting the diffusion process in generative diffusion models for improving the calibration of the discriminator during training. Our DifAugGAN can be a Plug-and-Play strategy for current GAN-based SISR methods to improve the calibration of the discriminator and thus improve SR performance.
  arXiv  Detail & Related papers  (2023-11-30T12:37:53Z)
• Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting [58.91947205027892]
  Federated learning has exhibited vulnerabilities to Byzantine attacks. Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and performance of the global model. A wealth of robust AGgregation Rules (AGRs) have been proposed to defend against Byzantine attacks.
  arXiv  Detail & Related papers  (2023-02-13T03:31:50Z)
• FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs [24.18718734850797]
  Data-Efficient GANs (DE-GANs) aim to learn generative models with a limited amount of training data. Contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs. We propose FakeCLR, which only applies contrastive learning on fake samples.
  arXiv  Detail & Related papers  (2022-07-18T14:23:38Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.