離散STDP学習を用いたレート符号化スパイキングニューラルネットワークによる視覚場所認識
Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning
スパイキングニューラルネットワークとSTDP学習を用いた視覚ループ閉鎖問題に対し、閉形式の決定論的テンソルパイプラインによるニューロン割り当てや状態リセット、速度補償スライディングウィンドウ集約などの推論段階の設計が再現率に与える影響を調査し、高精度を達成した。
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著者: Altzi Tsanko, Oikonomou Katerina Maria, Antonios Gasteratos
分類: cs.NE, cs.CV, cs.RO
原文アブストラクト
Spiking Neural Networks (SNNs) trained through unsupervised Spike-Timing-Dependent Plasticity (STDP) have been explored as solutions to visual loop closure problems, driven by the prospect of efficient on-device inference on neuromorphic devices. State-of-the-art STDP-based models deliver high classification accuracy but fail to reach the high Recall at 100% Precision (R@100P) needed for reliable autonomous navigation. We present a discrete, tensor-native implementation of the STDP-based SNN-VPR pipeline using PyTorch with snnTorch and evaluate it on a 100-place Nordland dataset using 15 independently-trained networks. The contribution of three decisions in the implementation is investigated. First, we show how to perform neuron assignment with a closed-form, deterministic tensor pipeline and show that it provides significantly higher R@100P than a standard argmax procedure. However, some of this gain comes from implementation differences compared to prior continuous-time models, which we measure independently. Second, ablation in isolation shows that state reset after each query helps improve R@100P regardless of the way neurons are assigned. Third, velocity-compensated sliding window aggregation over k consecutive frames reaches R@100P = 100.00% at k = 5 for constant-velocity traversal and an additional 0.20 ms latency. Taken together, these findings show the impact of inference stage design decisions in STDP-based SNN-VPR on recall precision, although the separate contribution of each mechanism and implementation differences is only partially disentangled and needs further examination.
