MMCGAN: Generative Adversarial Network with Explicit Manifold Prior

• URL: http://arxiv.org/abs/2006.10331v1
• Date: Thu, 18 Jun 2020 07:38:54 GMT
• Title: MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
• Authors: Guanhua Zheng, Jitao Sang, Changsheng Xu
• Abstract summary: We propose to employ explicit manifold learning as prior to alleviate mode collapse and stabilize training of GAN. Our experiments on both the toy data and real datasets show the effectiveness of MMCGAN in alleviating mode collapse, stabilizing training, and improving the quality of generated samples.
• Score: 78.58159882218378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training. In this work, we propose to employ explicit manifold learning as prior to alleviate mode collapse and stabilize training of GAN. Since the basic assumption of conventional manifold learning fails in case of sparse and uneven data distribution, we introduce a new target, Minimum Manifold Coding (MMC), for manifold learning to encourage simple and unfolded manifold. In essence, MMC is the general case of the shortest Hamiltonian Path problem and pursues manifold with minimum Riemann volume. Using the standardized code from MMC as prior, GAN is guaranteed to recover a simple and unfolded manifold covering all the training data. Our experiments on both the toy data and real datasets show the effectiveness of MMCGAN in alleviating mode collapse, stabilizing training, and improving the quality of generated samples.

Related papers

• Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data [4.681760167323748]
  We introduce a framework for learning a probabilistic generative model and the underlying, nonlinear data manifold directly from corrupted observations.<n>We establish theoretical guarantees showing that, under appropriate geometric regularization and measurement conditions, the learned model recovers the underlying data distribution up to a controllable error and yields a smooth, bi-Lipschitz manifold parametrization.
  arXiv  Detail & Related papers  (2026-01-26T17:51:52Z)
• Model Merging via Multi-Teacher Knowledge Distillation [11.543771846135021]
  We introduce a novel flatness-aware PAC-Bayes generalization bound specifically for the model merging setting.<n>We frame model merging as multi-teacher knowledge distillation on scarce, unlabeled data.<n>We formally demonstrate that minimizing the student-teacher Kullback-Leibler divergence directly tightens the upper bound on the merged model's excess risk.
  arXiv  Detail & Related papers  (2025-12-24T17:10:44Z)
• Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification [59.59359638389348]
  We propose a Dual-level Modality Debiasing Learning framework that implements debiasing at both the model and optimization levels.<n>Experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
  arXiv  Detail & Related papers  (2025-12-03T12:43:16Z)
• SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions [5.204468049641428]
  We propose ScoreNF, a score-based learning framework built on the Normalizing Flow architecture.<n>We show that ScoreNF maintains high performance even with small training ensembles.<n>We also present a method for assessing mode-covering and mode-collapse behaviours.
  arXiv  Detail & Related papers  (2025-10-24T10:43:19Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency [52.52950138164424]
  We show that when leveraging the off-the-shelf (vision) foundation models for feature extraction, the geometric shapes of the resulting feature distributions exhibit remarkable transferability across domains and datasets.<n>We embody our geometric knowledge-guided distribution calibration framework in two popular and challenging settings: federated learning and long-tailed recognition.<n>In long-tailed learning, it utilizes the geometric knowledge transferred from sample-rich categories to recover the true distribution for sample-scarce tail classes.
  arXiv  Detail & Related papers  (2025-08-19T05:22:59Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions [31.75902683077129]
  The Restricted Boltzmann Machine (RBM) is one of the simplest generative neural networks capable of learning input distributions.<n>We simplify the standard RBM training objective into a form that is equivalent to the multi-index model with non-separable regularization.<n>We show in particular that RBM reaches the optimal computational weak recovery threshold, aligning with the BBP transition.
  arXiv  Detail & Related papers  (2025-05-23T15:51:46Z)
• End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler [15.372235873766812]
  Learnable mixture priors offer improved control over exploration, adaptability to target support, and increased to counteract mode collapse. Our experimental results demonstrate significant performance improvements across a diverse range of real-world and synthetic benchmark problems.
  arXiv  Detail & Related papers  (2025-03-01T14:58:14Z)
• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• Variational Density Propagation Continual Learning [0.0]
  Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data. This paper proposes a framework for adapting to data distribution drift modeled by benchmark Continual Learning datasets.
  arXiv  Detail & Related papers  (2023-08-22T21:51:39Z)
• Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study [16.07380451502911]
  This paper revisits the robustness of the minimum error entropy criterion to deal with non-Gaussian noises. We investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common.
  arXiv  Detail & Related papers  (2023-07-17T15:38:11Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• Guided Deep Metric Learning [0.9786690381850356]
  We propose a novel approach to DML that we call Guided Deep Metric Learning. The proposed method is capable of a better manifold generalization and representation to up to 40% improvement.
  arXiv  Detail & Related papers  (2022-06-04T17:34:11Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• A new perspective on probabilistic image modeling [92.89846887298852]
  We present a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMMs can be trained end-to-end by SGD from random initial conditions, much like CNNs. We show that DCGMMs compare favorably to several recent PC and SPN models in terms of inference, classification and sampling.
  arXiv  Detail & Related papers  (2022-03-21T14:53:57Z)
• 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)
• Evolving parametrized Loss for Image Classification Learning on Small Datasets [1.4685355149711303]
  This paper proposes a meta-learning approach to evolving a parametrized loss function, which is called Meta-Loss Network (MLN) In our approach, the MLN is embedded in the framework of classification learning as a differentiable objective function. Experiment results demonstrate that the MLN effectively improved generalization compared to classical cross-entropy error and mean squared error.
  arXiv  Detail & Related papers  (2021-03-15T10:00:18Z)

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.