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SCRAPL: Scattering Transform with Random Paths for Machine Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
scattering transformwaveletsstochastic optimizationddspperceptual quality assessment
Abstract:

The Euclidean distance between wavelet scattering transform coefficients (known as paths) provides informative gradients for perceptual quality assessment of deep inverse problems in computer vision, speech, and audio processing.
However, these transforms are computationally expensive when employed as differentiable loss functions for stochastic gradient descent due to their numerous paths, which significantly limits their use in neural network training. Against this problem, we propose ``Scattering transform with Random Paths for machine Learning'' (SCRAPL): a stochastic optimization scheme for efficient evaluation of multivariable scattering transforms. We implement SCRAPL for the joint time–frequency scattering transform (JTFS) which demodulates spectrotemporal patterns at multiple scales and rates, allowing a fine characterization of intermittent auditory textures. We apply SCRAPL to differentiable digital signal processing (DDSP), specifically, unsupervised sound matching of a granular synthesizer and the Roland TR-808 drum machine. We also propose an initialization heuristic based on importance sampling, which adapts SCRAPL to the perceptual content of the dataset, improving neural network convergence and evaluation performance. We make our audio samples available and provide SCRAPL as a Python package.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes SCRAPL, a stochastic optimization scheme for efficiently evaluating scattering transform losses during neural network training, with applications to audio synthesis tasks. Within the taxonomy, it occupies the 'Stochastic Path Sampling for Scattering Transforms' leaf under 'Scattering Transform Loss Optimization Methods'. Notably, this leaf contains only the original paper itself—no sibling papers appear in the same category. This suggests the specific approach of random path sampling for scattering transform optimization represents a relatively sparse research direction within the broader field of transform-based loss functions.

The taxonomy reveals neighboring work in 'Signal Reconstruction from Scattering Coefficients' (one paper) and 'Hybrid Perceptual-Neural-Physical Loss Functions' (two papers). These adjacent leaves address related but distinct problems: inverting scattering transforms versus combining multiple perceptual metrics. The broader 'Application Domains' branch (three papers across remote sensing, aerosol classification, and geophysics) demonstrates that scattering transforms find use beyond audio, yet none of these applications focus on the optimization efficiency challenges that SCRAPL targets. The taxonomy structure indicates that while scattering transforms appear across diverse domains, methods specifically addressing their computational cost during gradient descent remain underexplored.

Among the three contributions analyzed, none were clearly refuted by the 22 candidates examined. The core SCRAPL scheme examined 7 candidates with 0 refutable matches; the path-wise optimizer variants (P-Adam, P-SAGA) examined 8 candidates with 0 refutations; and the θ-importance sampling heuristic examined 7 candidates with 0 refutations. This limited search scope suggests that within the top-22 semantically similar papers, no prior work directly anticipates the specific combination of stochastic path sampling, adaptive moment estimation, and importance-based initialization for scattering transform optimization. However, the modest candidate pool means potentially relevant work outside these 22 papers remains unexamined.

Based on the available signals—a singleton taxonomy leaf, zero refutations across 22 candidates, and distinct positioning relative to reconstruction and hybrid loss methods—the work appears to occupy a novel niche within scattering transform research. The analysis is constrained by the limited search scope and does not cover the broader stochastic optimization literature or alternative perceptual loss frameworks that might share conceptual overlap. A more exhaustive search could reveal related variance reduction techniques or sampling strategies in adjacent fields.

Taxonomy

Core-task Taxonomy Papers
6
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Efficient optimization of scattering transform loss for neural network training. The field centers on leveraging scattering transforms—multiscale, translation-invariant representations—as loss functions or feature extractors in neural network pipelines. The taxonomy reveals three main branches: methods for optimizing scattering transform losses, broader perceptual loss function design strategies, and diverse application domains where transform-based features prove valuable. The first branch focuses on computational techniques to make scattering-based objectives tractable during training, while the second explores how perceptual metrics (including but not limited to scattering) can guide learning toward human-relevant features. The third branch spans audio synthesis, geophysical prediction, and other specialized tasks where structured, multiscale representations offer advantages over raw pixel or waveform losses. Representative works such as Perceptual Sound Matching[2] and Neural Physical Sound[4] illustrate how scattering features enable perceptually grounded audio generation, while studies like Earthquake Geomagnetic Prediction[3] and Aerosol LSTM[5] demonstrate applicability beyond traditional media domains. A particularly active line of work addresses the computational bottleneck of evaluating full scattering transforms during backpropagation. SCRAPL[0] introduces stochastic path sampling to approximate scattering gradients efficiently, trading off exact computation for scalability. This contrasts with approaches that precompute or cache scattering coefficients, and with methods that rely on simpler perceptual proxies. Nearby efforts such as Scattering Reconstruction[6] explore how scattering features can be inverted or reconstructed, while CIRSM-Net[1] applies related multiscale ideas to specific imaging tasks. SCRAPL[0] sits squarely within the optimization-focused branch, emphasizing practical gradient estimation rather than architectural design or domain-specific tuning. Its stochastic sampling strategy offers a middle ground between full scattering evaluation and cruder approximations, aiming to preserve perceptual fidelity while enabling end-to-end training at scale.

Claimed Contributions

SCRAPL: Stochastic optimization scheme for scattering transforms

The authors introduce SCRAPL, a method that accelerates scattering transform computation during neural network training by stochastically sampling paths instead of computing all paths. This enables efficient use of scattering transforms as differentiable loss functions for gradient-based learning.

7 retrieved papers
Path-wise adaptive moment estimation (P-Adam) and path-wise SAGA (P-SAGA)

The authors develop two specialized optimization techniques that adapt existing algorithms (Adam and SAGA) to handle the unique structure of scattering transform paths, maintaining separate moment estimates and gradient memories for each path to reduce variance in stochastic gradient estimation.

8 retrieved papers
θ-importance sampling initialization heuristic

The authors propose an importance sampling method that constructs a non-uniform categorical distribution over scattering transform paths based on the sensitivity of each path to synthesizer parameters, improving convergence and evaluation performance by biasing path selection toward more informative gradients.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SCRAPL: Stochastic optimization scheme for scattering transforms

The authors introduce SCRAPL, a method that accelerates scattering transform computation during neural network training by stochastically sampling paths instead of computing all paths. This enables efficient use of scattering transforms as differentiable loss functions for gradient-based learning.

Contribution

Path-wise adaptive moment estimation (P-Adam) and path-wise SAGA (P-SAGA)

The authors develop two specialized optimization techniques that adapt existing algorithms (Adam and SAGA) to handle the unique structure of scattering transform paths, maintaining separate moment estimates and gradient memories for each path to reduce variance in stochastic gradient estimation.

Contribution

θ-importance sampling initialization heuristic

The authors propose an importance sampling method that constructs a non-uniform categorical distribution over scattering transform paths based on the sensitivity of each path to synthesizer parameters, improving convergence and evaluation performance by biasing path selection toward more informative gradients.

Table of Contents