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PAGER: Progressive Attribute-Guided Extendable Robust Image Generation

Zohreh Azizi, C. -C. Jay Kuo

arXiv:2206.00162v2cs.CVcs.AIcs.LGeess.IV

TL;DR

PAGER addresses the training, data, and interpretability challenges associated with deep-learning image generation by using successive subspace learning and a modular generative pipeline. The authors report comparable image quality with faster training and robustness to reduced training data, alongside conditional-generation applications.

  • Problem

    Deep-learning generative models can be time- and data-intensive, require careful design and tuning, and remain difficult to explain because of neural-network nonlinearity.

    Source: §1 · pp. 1–2

  • Method

    PAGER uses successive subspace learning instead of neural networks to model image features, with modules for low-resolution unconditional generation and conditional resolution and detail enhancement.

    Source: §1 · p. 2, §3.1 · p. 4, §6 · p. 15

  • Results

    Across MNIST, Fashion-MNIST, and CelebA, PAGER reports comparable image quality, faster training, robustness to fewer training samples, and conditional-generation applications.

    Source: §1 · p. 2, §4.4 · pp. 13–14

  • Takeaways & Limitations

    PAGER presents a modular, non-neural alternative for image generation that can be extended to conditional tasks while retaining image quality comparable to prior deep-learning techniques.

    Source: §4.4 · p. 13, §6 · p. 15

    PAGER has about 46 million parameters, primarily because its quality booster uses LLE to predict residual details, which is not optimized in the current implementation.

    Source: §6 · p. 15

Abstract

from arXiv · show

This work presents a generative modeling approach based on successive subspace learning (SSL). Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distribution and synthesize images. The resulting method, called the progressive attribute-guided extendable robust image generative (PAGER) model, has advantages in mathematical transparency, progressive content generation, lower training time, robust performance with fewer training samples, and extendibility to conditional image generation. PAGER consists of three modules: core generator, resolution enhancer, and quality booster. The core generator learns the distribution of low-resolution images and performs unconditional image generation. The resolution enhancer increases image resolution via conditional generation. Finally, the quality booster adds finer details to generated images. Extensive experiments on MNIST, Fashion-MNIST, and CelebA datasets are conducted to demonstrate generative performance of PAGER.

1 Introduction

PAGER replaces neural-network-based image generation with an SSL-based approach intended to address training cost, data requirements, and interpretability challenges. Its modular design supports unconditional generation and conditional image-generation tasks, with reported comparable quality, faster training, and robustness to fewer training samples.

1 Introduction

  • GANs are widely used for unconditional generation, but their training can be sensitive to hyperparameters, encounter convergence issues, and require substantial data and GPU resources.The passage also notes that limited data can cause discriminator overfitting and training divergence.Source: §1 · p. 1
  • Neural-network generative models can require long training, extensive data, and careful architecture and hyperparameter choices, while their nonlinearity makes their performance difficult to explain.The introduction also notes that architectures may need redesign for super-resolution or attribute-guided generation.Source: §1 · pp. 1–2
  • PAGER uses successive subspace learning (SSL) rather than neural networks and is presented as mathematically transparent, progressively generative, and extendable to conditional image generation.The authors also claim lower training time and robust performance with fewer training samples.Source: §1 · p. 2
  • The three modules generate low-resolution images unconditionally, increase resolution conditionally, and add finer details to the generated images.They are the core generator, resolution enhancer, and quality booster, respectively.Source: §1 · p. 2
  • Across MNIST, Fashion-MNIST, and CelebA, PAGER reports similar generation quality to deep-learning models while training in a fraction of the time.The experiments also examine reduced training-set sizes and applications including super-resolution and attribute-guided face generation.Source: §1 · p. 2

2 Related Work

The related work reviews deep-learning generation methods and their limitations, defines unconditional and conditional generation, and surveys successive subspace learning (SSL) and prior SSL-based generative models. It positions PAGER as a modular SSL alternative that can extend across generation tasks.

2.1 DL-based Image Generative Models

  • Deep-learning image generation includes adversarial models such as GANs and non-adversarial approaches such as VAEs, flows, GLANN, and diffusion models.The section notes that despite impressive results, deep-learning methods can be opaque, slow to train, susceptible to convergence problems, and dependent on large datasets.Source: §2.1 · p. 2

2.2 Unconditional and Conditional Image Generation

  • PAGER combines unconditional core generation with conditional resolution enhancement and quality boosting, and is presented as applicable to attribute-guided faces, super-resolution, and potentially very-high-resolution generation.The section describes prior approaches to attribute-guided faces, super-resolution, and progressive very-high-resolution generation.Source: §2.2 · p. 3

2.3 Successive Subspace Learning (SSL)

  • SSL cascades data-derived Saab transforms to extract multiscale joint spatial-spectral representations; the invertible transforms allow reconstruction from the resulting features.The section also lists SSL applications including image generation, point-cloud analysis, and face recognition.Source: §2.3 · p. 3

2.4 SSL-based Image Generative Models

  • Figure 1 shows marginal distributions for the first three RGB and Saab components above their joint distributions, using CelebA images.The figure contrasts RGB pixel distributions on the left with Saab-transform distributions on the right.Source: Figure 1 · p. 4
  • Unlike GenHop, which applies ICA and clustering to SSL features, PAGER models SSL-feature distributions directly with GMMs and is described as less computationally complex and memory-intensive.The authors also characterize PAGER as progressive and modular, with conditional, attribute-guided, super-resolution, and high-resolution generation applications.Source: §2.4 · pp. 3–4

3 Proposed PAGER Method

PAGER uses successive subspace learning (SSL) to map images into a core space, then progresses from unconditional low-resolution synthesis through conditional enhancement. Its method combines a Gaussian mixture model with resolution and quality modules, and extends to attribute-guided face generation.

3.1 Motivation

  • Saab-based SSL maps images to core-space feature vectors whose PCA components are uncorrelated, facilitating Gaussian-prior modeling.The paper uses a mixture of Gaussians to represent the core-space distribution because higher-dimensional vectors may not fit one multivariate Gaussian well.Source: §3.1 · p. 4

3.2 System Overview

  • Generation proceeds from an unconditional low-resolution core generator to conditional modules that increase image resolution and quality.The system overview depicts this as a progressive generation pipeline.Source: §3.2 · p. 5, Figure 2 · p. 5

3.2.1 Module 1: Core Generator

  • The core generator applies consecutive Saab transforms and fits a Gaussian mixture model (GMM) to the resulting core-space vectors.SSL features and GMMs are combined for density modeling, and the GMM parameters are determined with expectation maximization; generation is kept in a low-dimensional space because estimation becomes intractable and less accurate at large dimensions.Source: §3.2.1 · pp. 5–6

3.2.2 Module 2: Resolution Enhancer

  • The resolution enhancer interpolates a low-resolution image, classifies its DC component into a GMM cluster, and samples the associated AC residual conditionally.It converts the sampled residual through inverse SSL, masks it using Canny-derived edge information to suppress noise and additions to smooth regions, then recursively enhances cropped regions until a 2 × 2 window is reached.Source: §3.2.2 · p. 6

3.2.3 Module 3: Quality Booster

  • The quality booster uses locally linear embedding (LLE) to estimate residuals from stored training examples and add detail and texture to enhanced images.The authors state that this residual-based sharpening does not significantly affect diversity, and that LLE can combine patch instances into diverse patterns.Source: §3.2.3 · pp. 6–7

3.3 Attribute-Guided Face Image Generation

  • For attribute-guided face generation, seven CelebA attributes are clustered into ten models, and cosine-nearest-model sampling is reported to preserve target attributes, diversity, and fidelity.The clustering addresses attribute combinations that may be sparse because attributes are correlated.Source: §3.3 · p. 7

4 Experiments

Experiments on MNIST, Fashion-MNIST, and CelebA evaluate image quality, sample-size robustness, training time, and image-generation comparisons. PAGER achieves FID scores comparable to prior models while training faster and remaining less sensitive to reduced training data.

4.1 Experimental Setup

  • The experiments cover MNIST, Fashion-MNIST, and CelebA; face generation scales from 4×4 to 32×32, while digit and clothing generation starts at 16×16 before enhancement and cropping.The MNIST and Fashion-MNIST outputs are cropped to 28×28 after conversion to 32×32.Source: §4.1 · p. 7

4.2 Evaluation of Generated Image Quality

  • At 32×32 CelebA resolution, PAGER produces more realistic images with finer details than GenHop; the discussion links GenHop’s heavy LLE reliance to smoothing and reduced diversity.Unlike GenHop, PAGER is also described as supporting attribute-guided generation and super-resolution.Source: §4.2 · p. 8, Figure 5 · p. 10, §4.4 · p. 14
  • Across MNIST, Fashion-MNIST, and CelebA, PAGER’s FID scores are comparable to prior generative models, and generated samples show dataset-appropriate detail.The authors note that PAGER does not outperform prior models in FID and remains in preliminary development.Source: §4.2 · pp. 8–10, Table 1 · p. 10

4.3 Other Performance Metrics

  • At 1K MNIST training samples, PAGER’s FID is 16.2, better than original WGAN-GP, VAE, and GLO FID scores; CelebA quality drops negligibly at 5K versus 100K samples.PAGER is reported as least affected by training sample size in the MNIST comparison.Source: §4.3 · p. 12, Figure 7 · p. 12, Figure 10 · p. 14
  • CPU training takes slightly more than 4 minutes and GPU training around 3 minutes; PAGER is 11× faster than WGAN on CPU and 9× faster on GPU.The reported comparisons also give 325× faster than LSGAN on CPU and 48× faster than GLANN on GPU.Source: §4.3 · p. 12
  • In joint FID–training-time evaluation, PAGER significantly outperforms prior art and lies closest to the desirable low-FID, low-time corner.The figure describes points nearer the bottom-left as more desirable.Source: §4.3 · p. 12, Figure 9 · p. 13

5 Comments on Extendability

PAGER’s conditional modules can be reused for super-resolution without additional training, and its modular design supports attribute-guided and higher-resolution generation. The authors note that further optimization is needed for results comparable to state-of-the-art models.

Super Resolution

  • PAGER’s resolution enhancer and quality booster perform super-resolution without additional training, increasing resolution from 2^d × 2^d to 2^(d+k) × 2^(d+k).Figure 11 illustrates examples ending at 32 × 32, 64 × 64, and 128 × 128.Source: §5 · p. 14, Figure 11 · p. 15

Attribute-guided Face Image Generation

  • Attribute-guided face generation partitions training data by attributes and trains a separate core generator for each subset.CelebA-like generation’s mean FID changes only 6% when training samples fall from 100K to 5K.Source: §5 · p. 14

High-Resolution Image Generation

  • Cascading more resolution enhancers and quality boosters extends PAGER to higher-resolution generation, with examples at 128 × 128 and 256 × 256.The authors describe these examples as evidence of extendibility to higher resolutions.Source: §5 · p. 14
  • Further optimization is needed for generated images at higher resolutions to be comparable with state-of-the-art generative models.The authors state that further improvement could enhance image quality at higher resolutions.Source: §5 · p. 15

6 Conclusion and Future Work

PAGER uses successive subspace learning to model unconditional and conditional feature distributions, generating low-resolution images before progressively enhancing resolution and quality. The paper reports fast training, robustness as training samples decrease, and extension to several generation tasks, while identifying model size as a future optimization target.

Conclusion

  • PAGER uses successive subspace learning to extract multi-scale features and model their unconditional and conditional probability densities for image generation.Its core generator produces low-resolution images, while the resolution enhancer and quality booster progressively improve resolution and detail.Source: §6 · p. 15
  • The authors report that PAGER trains in a fraction of the time required by deep-learning models and maintains generation quality as training samples decrease.The conclusion also reports extension to super-resolution, attribute-guided face generation, and high-resolution image generation.Source: §6 · p. 15

Future Work

  • PAGER has about 46 million parameters, primarily because of the quality booster’s use of LLE to predict residual details.The authors propose replacing LLE with a lightweight alternative such as GMMs, aiming to reduce the model below 10 million parameters.Source: §6 · p. 15

Conclusion

  • Generated CelebA-like examples at 128 × 128 and 256 × 256 illustrate the model’s higher-resolution generation.The conclusion identifies high-resolution generation as one of PAGER’s extensions.Source: §6 · p. 15, Figure 12 · p. 16

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