DeepACTIF: Efficient Feature Attribution via Activation Traces in Neural Sequence Models
This work addresses the need for efficient real-time interpretability in domains like healthcare and biometrics, though it is incremental as it builds on existing attribution methods with a novel architecture-aware approach.
The paper tackles the problem of computationally intensive feature attribution methods for interpreting deep learning models in time-series domains by introducing DeepACTIF, a lightweight method that leverages internal activations in LSTM-based networks. The result shows that DeepACTIF preserves predictive performance under severe feature reduction (top 10% of features) and significantly outperforms established methods like SHAP and IG in accuracy and robustness, with reduced computation time and memory usage by orders of magnitude.
Feature attribution is essential for interpreting deep learning models, particularly in time-series domains such as healthcare, biometrics, and human-AI interaction. However, standard attribution methods, such as Integrated Gradients or SHAP, are computationally intensive and not well-suited for real-time applications. We present DeepACTIF, a lightweight and architecture-aware feature attribution method that leverages internal activations of sequence models to estimate feature importance efficiently. Focusing on LSTM-based networks, we introduce an inverse-weighted aggregation scheme that emphasises stability and magnitude of activations across time steps. Our evaluation across three biometric gaze datasets shows that DeepACTIF not only preserves predictive performance under severe feature reduction (top 10% of features) but also significantly outperforms established methods, including SHAP, IG, and DeepLIFT, in terms of both accuracy and statistical robustness. Using Wilcoxon signed-rank tests and effect size analysis, we demonstrate that DeepACTIF yields more informative feature rankings with significantly lower error across all top-k conditions (10 - 40%). Our experiments demonstrate that DeepACTIF not only reduces computation time and memory usage by orders of magnitude but also preserves model accuracy when using only top-ranked features. That makes DeepACTIF a viable solution for real-time interpretability on edge devices such as mobile XR headsets or embedded health monitors.