CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics

• URL: http://arxiv.org/abs/2504.05945v1
• Date: Tue, 08 Apr 2025 11:58:56 GMT
• Title: CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics
• Abstract summary: We propose a novel generative adversarial network (GAN) variant based on an integral probability metrics framework with characteristic kernel (CKIPM)<n> CKGAN mitigates the notorious problem of mode collapse by mapping the generated images back to random noise.<n>The experimental evaluation conducted on a set of synthetic and real image benchmarks (MNIST, CelebA, etc.) demonstrates that CKGAN generally outperforms other MMD-based GANs.
• Score: 9.637975962527511
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose CKGAN, a novel generative adversarial network (GAN) variant based on an integral probability metrics framework with characteristic kernel (CKIPM). CKIPM, as a distance between two probability distributions, is designed to optimize the lowerbound of the maximum mean discrepancy (MMD) in a reproducing kernel Hilbert space, and thus can be used to train GANs. CKGAN mitigates the notorious problem of mode collapse by mapping the generated images back to random noise. To save the effort of selecting the kernel function manually, we propose a soft selection method to automatically learn a characteristic kernel function. The experimental evaluation conducted on a set of synthetic and real image benchmarks (MNIST, CelebA, etc.) demonstrates that CKGAN generally outperforms other MMD-based GANs. The results also show that at the cost of moderately more training time, the automatically selected kernel function delivers very close performance to the best of manually fine-tuned one on real image benchmarks and is able to improve the performances of other MMD-based GANs.

Related papers

• A Coulomb Particle Model for Learning Kernel Attention in Transformers [1.7685947618629572]
  Randomized features provide a scalable approximation to kernel machines.<n>We propose a particle-based method that learns this distribution by optimizing kernel-target alignment.<n>We instantiate the method in linearized Transformer attention by learning positive random-feature maps in a first alignment phase.
  arXiv  Detail & Related papers  (2026-07-26T22:23:35Z)
• Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests [12.805268849262243]
  Maximum Maximum Discrepancy (MMD) is a statistic for nonparametric two-sample testing.<n>MMD is dictated entirely by the chosen kernel.<n>We propose Complexity-Penalized MMD (CP-MMD)<n>CP-MMD enables grid-free kernel selection across linear, parametric-feature regimes, and deep variance, matching or exceeding state-of-the-art test power.
  arXiv  Detail & Related papers  (2026-05-07T19:32:06Z)
• The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection [0.8536502059643899]
  We present a Bayesian optimization framework built on kernel-of- Kernels geometry.<n>A multidimensional scaling (MDS) embedding maps a discrete kernel library into a continuous Euclidean manifold, enabling smooth BO.<n>We demonstrate the approach on synthetic benchmarks, real-world time-series datasets, and an additive manufacturing case study.
  arXiv  Detail & Related papers  (2026-01-08T20:52:53Z)
• FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection [19.148841575715746]
  Coreset selection compresses datasets into compact, representative subsets, reducing the energy and computational burden of training deep neural networks.<n>We propose FAST, the first DNN-free distribution-matching coreset selection framework.<n>FAST significantly outperforms state-of-the-art coreset selection methods across all evaluated benchmarks, achieving an average accuracy gain of 9.12%.
  arXiv  Detail & Related papers  (2025-11-22T09:24:57Z)
• Universality and kernel-adaptive training for classically trained, quantum-deployed generative models [7.192684088403013]
  The instantaneous quantum (IQP) quantum circuit Born machine (QCBM) has been proposed as a promising quantum generative model over bitstrings.<n>Recent works have shown that the training of IQP-QCBM is classically tractable w.r.t. the so-called Gaussian kernel maximum mean discrepancy (MMD) loss function.<n>We show that in the kernel-adaptive method, the convergence of the MMD value implies weak convergence in distribution of the generator.
  arXiv  Detail & Related papers  (2025-10-09T17:17:34Z)
• Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks [0.18641315013048293]
  This paper proposes an interpretable working condition recognition method based on deep convolutional configuration networks (DCSCNs)<n>Supervised learning mechanism is employed to generate physically meaningful differential convolution kernels.<n>Joint reward function is constructed based on the recognition accuracy, the interpretable trustworthiness evaluation metrics, and the model parameter quantity.
  arXiv  Detail & Related papers  (2025-01-06T03:17:41Z)
• Optimal Kernel Choice for Score Function-based Causal Discovery [92.65034439889872]
  We propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms kernel selection methods.
  arXiv  Detail & Related papers  (2024-07-14T09:32:20Z)
• Variance-Reducing Couplings for Random Features [57.73648780299374]
  Random features (RFs) are a popular technique to scale up kernel methods in machine learning. We find couplings to improve RFs defined on both Euclidean and discrete input spaces. We reach surprising conclusions about the benefits and limitations of variance reduction as a paradigm.
  arXiv  Detail & Related papers  (2024-05-26T12:25:09Z)
• Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph [73.68184322526338]
  Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. This paper proposes a novel local sample-weighted multiple kernel clustering model. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
  arXiv  Detail & Related papers  (2022-07-05T05:00:38Z)
• Learning with convolution and pooling operations in kernel methods [8.528384027684192]
  Recent empirical work has shown that hierarchical convolutional kernels improve the performance of kernel methods in image classification tasks. We study the precise interplay between approximation and generalization in convolutional architectures. Our results quantify how choosing an architecture adapted to the target function leads to a large improvement in the sample complexity.
  arXiv  Detail & Related papers  (2021-11-16T09:00:44Z)
• Hybrid Random Features [60.116392415715275]
  We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) HRFs automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
  arXiv  Detail & Related papers  (2021-10-08T20:22:59Z)
• Continual Learning with Fully Probabilistic Models [70.3497683558609]
  We present an approach for continual learning based on fully probabilistic (or generative) models of machine learning. We propose a pseudo-rehearsal approach using a Gaussian Mixture Model (GMM) instance for both generator and classifier functionalities. We show that GMR achieves state-of-the-art performance on common class-incremental learning problems at very competitive time and memory complexity.
  arXiv  Detail & Related papers  (2021-04-19T12:26:26Z)
• Random Features for the Neural Tangent Kernel [57.132634274795066]
  We propose an efficient feature map construction of the Neural Tangent Kernel (NTK) of fully-connected ReLU network. We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice.
  arXiv  Detail & Related papers  (2021-04-03T09:08:12Z)
• Flow-based Kernel Prior with Application to Blind Super-Resolution [143.21527713002354]
  Kernel estimation is generally one of the key problems for blind image super-resolution (SR) This paper proposes a normalizing flow-based kernel prior (FKP) for kernel modeling. Experiments on synthetic and real-world images demonstrate that the proposed FKP can significantly improve the kernel estimation accuracy.
  arXiv  Detail & Related papers  (2021-03-29T22:37:06Z)
• Gaussian Process Latent Class Choice Models [7.992550355579791]
  We present a non-parametric class of probabilistic machine learning within discrete choice models (DCMs) The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs. The model is tested on two different mode choice applications and compared against different LCCM benchmarks.
  arXiv  Detail & Related papers  (2021-01-28T19:56:42Z)
• Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification [26.09932710494144]
  A new kernel learning method is proposed to improve the discrimination performance of Conditional Maximum Mean Discrepancy (CMMD) It can be operated with deep network features iteratively and thus denoted as KLN for abbreviation. In particular, the kernel-based similarities are iteratively learned on the deep network features, and the algorithm can be implemented in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-08-24T02:35:50Z)

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.