PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters

Edward Collier; Qun Liu; Supratik Mukhopadhyay

REVIEW 4 major objections 5 minor 23 references

PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Progressively training a classifier GAN from low to high resolution lets it classify noisy handwritten characters accurately without any denoising step.

desk verdict Smart combination of ACGAN and progressive GAN training, but the headline SOTA results are undermined by an input-resolution mismatch and a missing ablation. read the letter →

arxiv 1908.08987 v1 pith:5MNUR272 submitted 2019-08-11 cs.CV cs.IR

Qun Liu , Edward Collier , Supratik Mukhopadhyay This is my paper · ORCID

classification cs.CVcs.IR

keywords generativeadversarialnetworksprogressivegrowingnoisyhandwrittencharacterrecognitionBanglanumeralclassificationauxiliaryclassifierGANnoiserobustnesssparsefeaturesMNIST

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that a single classifier can recognize noisy handwritten characters directly from raw pixels, with no denoising step, by training a generative adversarial network progressively from low to high resolution. The claim is that learning the features of each resolution independently makes the model's discriminator robust to noise, because low-resolution features are generic and survive corruption better than fine-grained features. On the three benchmark tasks, the proposed model reports the best published accuracies on eight of nine noise-dataset combinations: for example 96.68% on Noisy Bangla Numeral with added white Gaussian noise, 79.85% on the 50-class Noisy Bangla Characters with the same noise, and 98.43% on Noisy MNIST. On the one exception, Noisy Bangla Characters under contrast noise, it reaches 68.41%, slightly below the prior best of 69.66%. If these results hold, noisy character recognition can skip a separate preprocessing stage entirely.

What carries the argument

The load-bearing mechanism is the progressively trained ACGAN discriminator, used as the classification network. ACGAN is a GAN variant whose discriminator has an auxiliary head predicting the input's class label in addition to the real/fake decision; progressive growing is the technique of training the generator and discriminator in stages at increasing spatial resolution, here 7x7, 14x14, and 28x28, copying the trained weights into the next stage. What this mechanism does is force the discriminator to learn generic, low-resolution features first and then finer features, so the final classifier has a set of resolution-specialized layers. Since low-resolution features are less disrupted by noise, the resulting classifier can label noisy raw inputs directly. The paper emphasizes that no denoising is performed at any point in training or inference.

What would settle it

Train the same ACGAN classifier at a fixed 28x28 resolution, with no progressive modules, for the same total number of epochs on Noisy Bangla Characters with AWGN noise. If that fixed-resolution model reaches or exceeds 79.85% accuracy, the progressive-training explanation is not what carries the result.

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Extended reading notes

Core claim

The central discovery is that progressive resolution training turns an Auxiliary Classifier GAN's discriminator into a noise-robust classifier. The model, built from an ACGAN in which the discriminator both distinguishes real from generated images and predicts class labels, is trained in three modules at 7x7, 14x14, and 28x28 resolution, with weights transferred from each module to the next. Because the generator supplies a stream of fake images, the discriminator sees examples ranging from noisy to sharp, and because it learns each resolution in isolation, its lower layers retain generic, noise-resistant features. The reported outcome is best-or-near-best accuracy on noisy Bangla numerals, noisy Bangla characters, and noisy MNIST under white Gaussian, motion-blur, and contrast noise, with statistically significant gains over a pixel-level-reconstruction baseline on the tested settings. The paper interprets these results as showing that an all-in-one classifier can work without explicit or implicit denoising.

Load-bearing premise

The paper's argument stands on the assumption that progressive resolution-by-resolution training, rather than the ACGAN architecture, the generator's fake-image curriculum, or longer training, is what gives the discriminator its noise robustness; the evaluation compares against prior published methods but does not include an ablation of the model's own components.

Editorial extensions

If this is right

Noisy handwritten character recognition can be treated as a single end-to-end classification problem: raw noisy pixels in, class label out, with no denoising preprocessing.
Progressive, resolution-by-resolution training appears to make GAN discriminators more robust to added image noise, a property that could generalize beyond character images.
On 10-class datasets (numerals and MNIST), the method's accuracy stays above 94% for all three noise types, while on the 50-class Bangla character set accuracy drops sharply, showing that class count remains the main difficulty.
The one reported miss, 68.41% versus 69.66% on contrast-degraded Bangla characters, shows the method does not dominate every noise type even when it leads on the other two.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If progressive training is the active ingredient, then an ablation that trains the same ACGAN at a fixed 28x28 resolution for the same total number of epochs should lose several accuracy points under AWGN and motion noise; the paper does not report such an ablation, so this is a testable prediction rather than a demonstrated result.
The same low-resolution-first idea could be ported to other sparse-feature recognition tasks, such as medical symbols, degraded printed text, or noisy CAPTCHAs, where current pipelines still denoise before classifying.
Because contrast noise is the weakest setting, a natural extension would be to add contrast-degraded real images to the discriminator's training batches at each progressive stage; if contrast robustness is a resolution-specialization issue, this should close the 1.25% gap.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper does something reasonable: it takes two existing ideas—ACGAN and progressive GAN training—and points them at noisy handwritten character classification, arguing that the discriminator becomes noise-robust without an explicit denoising step. The experiments cover three datasets and three noise types, and the reported numbers are mostly better than the cited baselines. That is a legitimate contribution, if the numbers hold up.

But the comparison as presented is not input-matched. The Bangla datasets are 32×32; the paper resizes them to 28×28 before feeding them to PCGAN-CHAR. The baselines in Tables 1–2 are compared at their native 32×32. Downsampling is a low-pass operation, and for AWGN and Motion blur it removes exactly the kind of high-frequency noise that depresses classification accuracy. The result pattern is consistent with this confound: the largest gains are on Motion and AWGN for Bangla Characters (5.95%, 3.11%), the smallest on MNIST, which is already 28×28 (0.62–2.21%). The paper never states baseline input resolutions or controls for this. I would not trust the claimed SOTA margins on the Bangla datasets without a matched-resolution comparison.

The other soft spot is the mechanism claim. Section 3.2 attributes the noise-robustness to progressive, resolution-by-resolution training. There is no ablation comparing PCGAN-CHAR against the same ACGAN discriminator trained directly at 28×28. For all we know, the gains could come from the auxiliary classifier, exposure to generator outputs, dropout, or longer training. The McNemar tests are also incomplete: no Motion tests are reported for any dataset, and no Contrast test for Bangla Characters—so the largest claimed margin is the one with no significance evidence. No error bars, no code, no hyperparameters.

On the positive side, the idea of training an all-in-one classifier on noisy data is sensible, the dataset coverage is decent, and the writing is straightforward. The McNemar tests that do appear are appropriate for paired test sets. The paper is not circular or incoherent; it is under-controlled.

This is worth sending to a serious referee, but the verdict should be conditional on the authors fixing the resolution mismatch, adding a non-progressive ACGAN ablation, and releasing code or at least the exact train/test splits and hyperparameters. As it stands, the paper is a useful cautionary example for empirical comparisons more than a reliable new benchmark.

For a reading group, it would spark a good discussion about confounds in applied deep learning papers. I would not cite the Bangla accuracy numbers in my own work until the matched-resolution results are out.

Referee Report

4 major / 5 minor

Summary. The paper proposes PCGAN-CHAR, a progressively trained auxiliary classifier GAN for classifying noisy handwritten Bangla characters and digits. The discriminator is trained at increasing resolutions (7×7, 14×14, 28×28) and then used as a classifier, with the claim that low-resolution generic features are noise-resistant and that the method can classify raw noisy data without a separate denoising step. The authors report accuracy on three datasets (Noisy Bangla Numeral, Noisy Bangla Characters, Noisy MNIST) under three noise types (AWGN, Contrast, Motion), compare against prior published methods, and provide McNemar tests for several conditions. The headline results are state-of-the-art accuracy on most conditions, with the largest gains on the Bangla datasets.

Significance. If the claims are supported, the work has practical value: an all-in-one classifier that avoids explicit denoising could simplify noisy-character pipelines, and progressive training as a robustness mechanism is an interesting idea. The paper is also commendable for evaluating on multiple datasets and noise types and for including statistical significance tests on several comparisons. However, the significance currently rests on two unverified pillars: (i) that the reported gains come from progressive training rather than from the ACGAN architecture or from input preprocessing, and (ii) that the comparisons to prior work are input-matched. The paper does not provide the ablation or controlled comparison needed to establish these points, so the empirical contribution is not yet convincingly separated from known confounds. The strengths are the breadth of experiments, the direct classification setup, and the presence of significance tests; the weaknesses are the missing ablation and the unresolved resolution mismatch.

major comments (4)

[§4.1–4.2, Tables 1–2] The comparison is not input-matched. Section 4.1 states that the Bangla Numeral and Character images are 32×32, while Section 4.2 states that they are resized to 28×28 before being fed to PCGAN-CHAR. Downsampling is a low-pass operation and attenuates independent additive noise, so PCGAN-CHAR is effectively evaluated on less noisy inputs than the baselines if the baselines used the original 32×32 images, as the cited works appear to have done. The paper does not state the input resolution used for Basu et al. [3], Dropconnect [13], or Karki et al. [13], nor does it report results for those baselines on resized inputs. This confound is especially relevant to the largest claimed margins (AWGN +3.11%, Motion +5.95% on Bangla Characters) and to the only loss (Contrast −1.25%), whose multiplicative component would survive downsampling differently. Please add a controlled comparison: evaluate the baselines on 28×28 inputs, or evaluate PCGAN-CHAR on 32×32 inputs, or both, and report the resulting accuracies.
[§3.2, Algorithm 1, Tables 1–3] There is no ablation that isolates progressive training. The paper's central mechanism claim is that resolution-by-resolution training makes the discriminator noise-robust (Section 3.2), but the reported experiments only compare against prior published methods. The natural control is the same ACGAN discriminator trained directly at 28×28 without progressive stages, with all other components (architecture, data, loss weights, training budget) held fixed. Without this ablation, the gains cannot be attributed to progressive training rather than to the auxiliary classifier, data augmentation implicit in the GAN objective, or longer training. Please add this ablation, and ideally also a version without the auxiliary classifier, to support the stated mechanism.
[§1 and §4.2] The claim that the framework 'can directly classify raw noisy data without any preprocessing' is inaccurate as stated. Resizing 32×32 images to 28×28, as described in Section 4.2, is itself a preprocessing operation, and for the noise types considered here it tends to suppress noise. This is not 'denoising' in the sense of a learned reconstruction, but it is a resolution change that can affect accuracy. Please either train and evaluate at the native 32×32 resolution, or characterize the effect of resizing on the reported accuracies, or revise the claim to acknowledge this preprocessing step.
[§4.3 and Implementation details] The empirical evaluation omits information needed to assess reliability and reproducibility. The paper does not report learning rate, batch size, optimizer, number of epochs per module, dropout rate, filter counts, or the relative weights of Ldiscern and Lclass in Algorithm 1. It also reports a single run per condition with no error bars. This is a particular concern for claimed gains as small as 0.62% (Noisy MNIST Motion), where run-to-run variation could change the conclusion. Please provide the full hyperparameter settings, the training schedule, and repeated-run statistics (mean and standard deviation, or confidence intervals) for at least the main comparisons.

minor comments (5)

[§4.3] The McNemar test results are reported only for a subset of conditions: no test statistic or p-value is given for the Motion-noise conditions on any dataset, and for Bangla Characters only the AWGN condition is tested. Please complete the reporting or explain the omission.
[§3.1, Eq. (1)] The notation in Eq. (1) is unclear: the term D(x|G(x|z)) appears to mix the generator input and output. It should presumably be D(G(z)), or the conditional dependencies should be defined explicitly.
[Algorithm 1] In Algorithm 1, line 5 samples a batch of n random vectors, but line 8 uses G(Bz,Bl) before Bl is sampled on line 7; later, line 9 samples 2n vectors. This is confusing and should be rewritten so that the batching order and the arguments to G are consistent.
[Throughout] There are typographical errors, including 'General Adversarial Networks' in the keywords and abstract, 'disrcriminator' in Section 2, and 'T able 1' in the table heading. A careful proofread is needed.
[Fig. 1] The architecture diagram does not clearly indicate which layers are shared or how the progressive module weights are transferred. A more detailed description in the caption or a separate table of layer specifications would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claims are empirical accuracy measurements, and its method component (progressive ACGAN training) is borrowed from external prior work rather than derived from its own conclusions.

full rationale

PCGAN-CHAR's contribution is an experimental accuracy comparison on noisy handwritten-character benchmarks. The paper does not derive a result from an equation that assumes the conclusion, and it does not fit a parameter to a subset of the evaluation data and then rename that fit a prediction. The progressive-growing technique is explicitly attributed to Karras et al. [14] and the ACGAN formulation to Odena et al. [20], both external sources. The only self-citation is Karki et al. [13], which is used as the published baseline and as the source of the noisy datasets; comparing against one's own earlier published method is a normal and legitimate baseline practice, not a load-bearing circularity. The strongest methodological caveat—that no ablation isolates progressive training from the ACGAN architecture or input resizing—concerns internal validity and attribution of the observed gains, not circular reasoning. Since the accuracy numbers are measured, not constructed from the method's own assumptions, no step in the paper's argument reduces to its inputs by definition.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new formal objects. It relies on standard deep-learning practice and on the empirical claim that progressive training improves noise robustness. The main unstated load is that the reported accuracy gains are caused by progressive training; without an ablation, this is an assumption, not a demonstrated result.

free parameters (3)

Training hyperparameters (learning rate, batch size, optimizer, number of epochs, dropout rate, filter counts)
The paper does not report these, yet they determine the reported accuracies.
Number of progressive modules and resolution schedule = 3 stages, 7x7, 14x14, 28x28
Chosen by hand; no ablation shows this schedule is optimal or that the choice matters.
Loss weighting for Ldiscern and Lclass in Algorithm 1 = Equal weights as shown in the update equations
The update equations treat Ldiscern and Lclass as equally weighted, but this weighting is not justified or varied.

assumptions (5)

domain assumption Progressive growing, as introduced by Karras et al., can be transferred from a generative model to a classifier GAN without loss of stability or performance.
Section 3.2 assumes the progressive training technique developed for image synthesis also works for a classifier discriminator.
domain assumption Low-resolution features are more resistant to noise and are learned independently at each resolution.
The motivating idea of the paper; stated in Section 1 and Section 3.2, but no experiment isolates this effect.
domain assumption The ACGAN loss (L1+L2 for the discriminator, L2-L1 for the generator) provides a discriminator with better classification and robustness than a plain CNN.
The paper relies on Odena et al.'s ACGAN formulation; it is treated as given.
standard math Standard backpropagation and stochastic gradient descent converge to a good solution for this architecture.
The training algorithm in Section 3.2 assumes standard neural network optimization.
domain assumption Resizing Bangla images from 32x32 to 28x28 does not remove class-discriminative information.
Section 4.2 states the images were resized; this could affect accuracy but is not analyzed.

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Cite this review

Pith. "Pith review of PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters." pith.science (2026). https://pith.science/paper/5MNUR272

@misc{pith2026190808987,
  author       = {Pith},
  title        = {Pith review of: PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5MNUR272}},
  note         = {Machine review of arXiv:1908.08987}
}

read the original abstract

Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the data before classification. In this paper, we consolidate the approach by training an all-in-one model that is able to classify even noisy characters. For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise. We experimentally demonstrate the effectiveness of our approach by classifying noisy versions of MNIST, handwritten Bangla Numeral, and Basic Character datasets.

Figures

Figures reproduced from arXiv: 1908.08987 by the authors.

**Figure 1.** Overview of our proposed Progressively Trained Classifier Generative Adversarial Networks (PCGAN-CHAR) architecture. This is the case for many real world images which contain various sources of noise that can distort their true representation. Such noise can easily reduce the quality of classifications and challenge the power of classifiers [22] [19]. Most algorithms include denoising step for the images before cla… view at source ↗

**Figure 2.** Sample data for different types of added noise for Noisy Bangla Characters. Three types of noisy data, Added White Gaussian Noise, Reduced Contrast with white gaussian noise, Motion Blurred noise, shown above from top to bottom. high resolution input. When handling high resolution data it can be too easy for the GAN to discriminate between the fake, generally low resolution imagery, and the real, which are high reso… view at source ↗

**Figure 3.** The classification accuracy of our approach. Our approach has been evaluated on datasets of Noisy Bangla Numeral, Noisy Bangla Characters, and Noisy MNIST with three types of added noise, AWGN, Motion, Contrast [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

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