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REVIEW 3 major objections 5 minor 68 references

Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Noise-injection training beats event filtering for DNN classifiers

desk verdict Useful, broad empirical study of noise-injection training for event-based vision, but missing key hyperparameters and a train/test noise overlap mean the robustness claim is under-specified. read the letter →

arxiv 2506.03918 v1 pith:EE3OM4M4 submitted 2025-06-04 cs.CV

Marcin Kowalczyk , Kamil Jeziorek , Tomasz Kryjak This is my paper
classification cs.CV
keywords event-basedvisionnoiseinjectiondataaugmentationPoissonshoteventcamerarobustclassificationspikingneuralnetworksgraphconvolutional
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

The paper tries to establish that deliberately adding synthetic noise to event-camera training data is a better way to make deep networks robust to sensor noise than filtering the noise out. The authors simulate shot noise with a Poisson-like process, add it to training samples at random intensities, and test four model families (convolutional, vision-transformer, spiking, and graph-convolutional networks) on three event-based classification datasets across noise levels from 0.01 to 5 Hz/px. They find that this noise-injection training keeps accuracy nearly flat across the whole noise range, while models trained on clean or fixed-noise data degrade sharply and three standard filtering algorithms remove useful events. The method achieves the highest average accuracy in almost every configuration, at the cost of at most about one percentage point on perfectly clean data. If correct, this offers a simpler alternative to event filtering that preserves all true events and avoids extra preprocessing latency.

What carries the argument

The load-bearing object is the synthetic noise generator and the random-intensity augmentation schedule built on it. Shot noise is modelled by dividing time into steps of length $\Delta t = 1/(\lambda N D)$, where $\lambda$ is the desired intensity in Hz per pixel, $N$ is the pixel count, and $D$ stretches the step so the per-pixel event probability $P = \lambda\Delta t = 1/(N D)$ stays far below one; at each step a random pixel is chosen to emit an event with probability $P$. During noise-injection training, each sample gets a randomly drawn noise level, including zero, so the network sees clean, lightly corrupted, and heavily corrupted versions of the same data. This forces features to be predictive of the object rather than of the noise floor, which is why the accuracy curves stay flat.

What would settle it

Record event streams from a real event camera under controlled temperature and bias settings, measure the actual noise statistics, then compare noise-injection-trained models with filtered baselines on those streams; if the accuracy advantage shrinks or reverses on real noise that is spatially correlated or intensity-dependent, the central claim is weakened. The paper's own real-noise test covers only one dataset, so an independent multi-sensor test would settle the question.

Watch

Extended reading notes

Core claim

The central discovery is that exposure to varied noise during training itself is a form of regularisation that transfers across noise intensities. The paper proposes a training variant in which each loaded event sample is assigned a random noise intensity, including zero, and synthetic events generated by a Bernoulli approximation of a Poisson process are added to the sample. Across the three tested event-based classification datasets, this variant yields the highest mean accuracy over the tested noise spectrum for all four architectures, with standard deviations far below the other training variants. For convolution-based and transformer-based models, additionally filtering the test data only lowers accuracy, suggesting the learned representations already absorb the noise; for the graph-convolutional model, moderate nearest-neighbour or interpolation-based filtering still helps, but noise-injection training remains the best overall. The authors report the trade-off honestly: on completely noise-free test data, noise-injection-trained models are sometimes up to about one percentage point behind models trained on filtered data.

Load-bearing premise

The load-bearing premise is that the synthetic Poisson-like shot noise used for both training augmentation and test corruptions behaves like the real background activity of event cameras; if real noise is spatially correlated, intensity-dependent, or otherwise structured differently, the demonstrated robustness may not transfer to deployed systems.

Editorial extensions

If this is right

  • Event filtering can be dropped from the preprocessing pipeline for convolutional, transformer, and spiking classifiers without losing accuracy, removing filter latency and the risk of deleting true events.
  • Models trained with fixed noise intensity only help near that intensity, while variable noise injection generalises across the whole tested range of 0.01 to 5 Hz/px.
  • Combining noise-injection training with filtering is neutral to harmful for convolutional, transformer, and spiking networks, but the graph-convolutional model still gains from moderate filtering, so graph-based models may need a hybrid approach.
  • The robustness comes at a small, bounded cost: up to about one percent lower accuracy on perfectly clean inputs, which is the deliberate trade-off for stability.

Reading between the lines

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

  • If the same random-noise schedule were tuned to sensor-specific noise statistics, such as sampling intensities from a distribution matched to temperature or bias settings, the method could become a drop-in calibration step for deployed event cameras.
  • The supplementary detection experiment suggests the benefit is not limited to classification; object detection on noisy event streams may inherit the same stability without architectural changes.
  • Because the graph-convolutional model behaves differently, a promising follow-up is noise-adaptive graph construction or a learned hybrid that combines moderate filtering with noise-injection training specifically for graph representations.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper proposes a noise-injection training augmentation method for event-based vision classifiers. The idea is to add synthetic Poisson shot noise of randomly varying intensity to training event streams, so that models become robust to noise at test time without requiring explicit filtering. The authors evaluate the method on three event camera classification datasets (N-Caltech101, N-Cars, Mini N-ImageNet) with four architectures (CNN, ViT, SNN, GCN), compare against three filtering baselines (NN, EDnCNN, DIF), and report mean accuracy and standard deviation across a noise grid from 0.01 to 5 Hz/px. The paper claims that noise-injection training provides stable performance over the noise range and consistently outperforms filtering. Supplementary material includes detection results, a filter-parameter ablation, an experiment with real recorded camera noise on N-Caltech101, and timing measurements.

Significance. If the central claims hold, the method is a simple and potentially practical alternative to event-data filtering: it requires no extra inference-time computation and is architecture-agnostic. The strengths of the manuscript are its breadth of empirical evaluation (four architectures, three datasets, three filtering baselines, plus detection and real-noise experiments in the supplementary) and the release of code. The main robustness claim is plausible, but its status as a generalization result rather than an interpolation result depends crucially on the unstated training-noise sampler; as reported, the claim is not fully falsifiable. The real-noise supplementary experiment is a valuable check but covers only one dataset and shows only partial transfer for some architectures.

major comments (3)
  1. [Section 3.3 / Section 4.1 / Table 2] The distribution and range of the random noise intensities used in Noise-injection training are never specified. Section 3.3 states only that 'a random value was drawn to determine the corresponding noise intensity,' and Section 4.1 repeats 'various levels of noise.' Since the test grid in Fig. 2 and Table 2 spans 0.01 to 5 Hz/px, the near-flat accuracy curves and very small standard deviations of the Noise-injection row could be interpolation results if the training sampler draws from the same range. Please report the exact sampler (distribution, bounds, and whether the level is redrawn per sample or per epoch), and evaluate at noise levels outside the training range (e.g., 10 and 20 Hz/px) to demonstrate out-of-distribution robustness. The real-noise experiment in Section 10 only covers N-Caltech101 and shows the advantage narrowing for ViT at low noise and reversing for GCN around 1 Hz/px, so it does not settle this concern.
  2. [Section 3.1 / Section 4.1] The parameter D in Eq. (1) is never given a value, even though Eq. (2) says the Poisson approximation quality depends on it. The 'Filtered' training variant in Section 4.1 also does not state which of the three filter algorithms (NN, EDnCNN, or DIF) and which parameters were used to construct the filtered training set. Both omissions prevent replication of the central comparisons. Please specify D and describe the filtering procedure for the Filtered baseline.
  3. [Table 2 / Section 4.2] The standard deviations reported in Table 2 are computed across the noise levels, not across repeated training runs. Thus the claim that Noise-injection 'consistently outperforms' filtering and other training variants is based on single runs per configuration. Please report mean and standard deviation over at least 3-5 training seeds for the main results, or explicitly state that the study is single-run and soften the consistency claim accordingly.
minor comments (5)
  1. [Section 10 (supplementary)] In the supplementary, 'Filtrated' should be 'Filtered'.
  2. [Section 4.4 / Figure 4] The caption of Figure 4 says 'with an NN-based filter, and without any method,' but the figure labels show 'w/o NN' and 'Ours'; please make the caption and labels consistent.
  3. [Section 8 (supplementary)] The top-3 accuracy columns in Tables 5-8 are never discussed; please add at least one sentence of commentary or remove them.
  4. [Section 3.6 / Figure 6] The NN temporal window is set to 10000 µs in Section 3.6, but Figure 6 in the supplementary shows the optimal NN threshold as 5000 for the same dataset; please explain this discrepancy or justify the choice.
  5. [Section 5] The Discussion section acknowledges the trade-off of reduced accuracy on clean data and the GCN limitation; consider also noting there that the primary evaluation uses the same synthetic noise model for training and testing, which may overstate the method's real-world transfer.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the noise-injection evaluation shares a synthetic Poisson noise generator between training and test, but the robustness claim is supported by an external real-noise experiment and does not reduce to a fitted parameter or a load-bearing self-citation.

full rationale

The paper is an empirical benchmarking study; it contains no derivation that reduces to its own inputs. The only overlap that could look circular is that the Poisson shot-noise model (Eqs. 1-2) is used both to corrupt test data (Sec. 4.1) and to define the Noise-injection training augmentation (Sec. 3.3). This is a matched-distribution evaluation, not a self-definitional prediction: the claim is that training with varied noise levels improves accuracy under those noise conditions, which is a standard, falsifiable machine-learning claim. The supplementary real-noise experiment (Sec. 10) tests on real camera noise not generated by Eqs. 1-2 and shows the method remains best or competitive on N-Caltech101, providing external support beyond the synthetic noise model. The paper explicitly acknowledges limitations (Sec. 5): GCN accuracy still degrades with noise, and there is a small clean-accuracy trade-off. The unspecified distribution and bounds of the random noise-intensity sampler in Sec. 3.3 is a reproducibility and generalization concern (if the training sampler matches the test grid 0.01-5 Hz/px, the reported stability is interpolation rather than out-of-distribution robustness), but this is not circularity under the stated criteria. No load-bearing self-citations were found: the only self-citation, [31] for the DIF filter, is a baseline method, not a premise of the noise-injection claim. Therefore the circularity score is 0.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The method introduces no new entities; its assumptions are the Poisson noise model and the adequacy of existing event representations. The main unstated free parameter is the training noise intensity distribution, which is central to the claimed robustness.

free parameters (4)
  • Training noise intensity distribution = not reported
    Section 3.3 says a random noise intensity is drawn per sample, including the possibility of no noise, but the range and distribution are not specified. This is the key hyperparameter of the method and directly determines the robustness profile.
  • D parameter in noise model = not reported
    Appears in Eqs. (1) and (2) controlling the Bernoulli approximation of the Poisson process; its value is not given, and it affects the temporal granularity of injected noise.
  • NN filter temporal window = 10000 µs
    Chosen by hand in Section 3.6; affects the filtered test evaluations.
  • DIF filter parameters = filter length 15000 µs, scale 4, update factor 0.5
    Chosen by hand in Section 3.6; the supplementary includes a partial ablation over NN and DIF parameters.
assumptions (3)
  • domain assumption Event-camera shot noise can be modeled as an independent per-pixel Poisson process.
    Used in Section 3.1 to generate synthetic noise; the main evaluation relies on this model, with real-noise validation deferred to the supplementary.
  • standard math The Bernoulli-trials approximation with P = 1/(N D) sufficiently approximates the Poisson process.
    Eq. (2) states P << 1 is required; no explicit convergence check is reported.
  • domain assumption The architectures and event representations (Event Count Image, Voxel Grid, Event Spike Tensor, Voxel Graph) are adequate for classification.
    Section 3.4 adopts representations from cited works without modification, assuming they preserve the information needed for classification under noise.

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Cite this review

Pith. "Pith review of Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection." pith.science (2026). https://pith.science/paper/EE3OM4M4

@misc{pith2026250603918,
  author       = {Pith},
  title        = {Pith review of: Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EE3OM4M4}},
  note         = {Machine review of arXiv:2506.03918}
}
read the original abstract

Event-based sensors offer significant advantages over traditional frame-based cameras, especially in scenarios involving rapid motion or challenging lighting conditions. However, event data frequently suffers from considerable noise, negatively impacting the performance and robustness of deep learning models. Traditionally, this problem has been addressed by applying filtering algorithms to the event stream, but this may also remove some of relevant data. In this paper, we propose a novel noise-injection training methodology designed to enhance the neural networks robustness against varying levels of event noise. Our approach introduces controlled noise directly into the training data, enabling models to learn noise-resilient representations. We have conducted extensive evaluations of the proposed method using multiple benchmark datasets (N-Caltech101, N-Cars, and Mini N-ImageNet) and various network architectures, including Convolutional Neural Networks, Vision Transformers, Spiking Neural Networks, and Graph Convolutional Networks. Experimental results show that our noise-injection training strategy achieves stable performance over a range of noise intensities, consistently outperforms event-filtering techniques, and achieves the highest average classification accuracy, making it a viable alternative to traditional event-data filtering methods in an object classification system. Code: https://github.com/vision-agh/DVS_Filtering

Figures

Figures reproduced from arXiv: 2506.03918 by the authors.

Figure 1
Figure 1. A simplified diagram of the proposed solution. Standard [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Results of different models evaluated on the N-Caltech101 [ [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. True-positive and false-positive rates for the NN, ED [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of models trained with our noise-injection method, with an NN-based filter, and without any method [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 6
Figure 6. Figure 6: Comparison of true-positive and false-positive rate val [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Results of classification on the N-Caltech101 [ [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison using GradCAM and GradCam++ between trained CNN model on Mini N-ImageNet with and without [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison using GradCAM and GradCam++ between trained CNN model on N-Caltech101 with and without our [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

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Reviewed August 7, 2026 · model on record in the stance chip above.