Newest papers

Forward-Only Learning for Shared-Kernel Wavelet Neural Networks without Backpropagation

Yongfei Guo, Xuan Sun, Ang Li, Zhao Zhang, Yuanhao Gong · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29697 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

バックプロパガンダは、深いネットワークにおけるバックグラディンが消滅するか爆発する可能性のあるバックグラディンとローカルアップデートを使用して支配的なトレーニングパラダイムであるが、バックプロパガンダは従来のバックプロパガンダを避けるが、主にローカルまたはレイヤーモードで動機づけ、前進評価のみを使用してグローバルネットワーク全体の学習を動機づけている。共用コアウォレットニューラルネットワークでは、CMA-ESベースのグローバルフラディンのみ学習枠組みを提案する。我々はまた、構造意識のパラメータ分解とチャネル特有のクレジット配分を導入し、検索を3つの

Machine translation (Meta M2M100); the English original follows.

Abstract

Backpropagation remains the dominant training paradigm, using backward gradients and local updates that can suffer from vanishing or exploding gradients in deep networks. Although the Forward-Forward algorithm avoids conventional backpropagation, it remains mainly local or layer-wise, motivating global whole-network learning using forward evaluations only. For shared-kernel wavelet neural networks, we therefore propose a CMA-ES-based global forward-only learning framework. We further introduce structure-aware parameter decomposition and channel-specific credit assignment, factorizing the search into three subspaces. On BSDS500, our method reduces test MSE from 4.1783 to 4.0130 and improves PSNR from 42.621 to 42.787~dB over Adam/BP without backward propagation. Beyond these gains, training requires only 6 training-set coverages, compared with 1500 epochs for BP. Ablations show that structural decomposition and aligned credit assignment are more important than increasing population size, demonstrating the feasibility of whole-network forward-only learning for compact reconstruction networks. These results suggest a potential route toward forward-only optimization of compact reconstruction models for resource-constrained imaging and edge or near-sensor systems.

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Topics: Backpropagation, Artificial intelligence, Artificial neural network, Computer science, Wavelet, Pattern recognition (psychology)

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