Convolutional Neural Networks

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Convolutional Neural Networks

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Perforated Neural Networks for Keyword Spotting
May 15, 2026Vishy Gopal, Aris Ilias Goutis, Ralph Crewe +2Heterogeneous Edge Devices Edge
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ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing
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May 14, 2026Chengshuai Yang, Lei Xing, Keyaan Zawad Alam +4Video Capsule Endoscopy Efficientnet Encoder
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May 14, 2026Lukas Schelenz, Shobha Rajanna, Denis Gosalci +6Trajectory Prediction Temporal Convolutional Networks
Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
May 14, 2026Suorong Yang, Hanqi Zhu, Hai Gan +4Adaptive Sampling Regularization Techniques
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How to Evaluate and Refine your CAM
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Deep Image Segmentation via Discriminant Feature Learning
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Local Spatiotemporal Convolutional Network for Robust Gait Recognition
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May 12, 2026Raj Kiran Gupta KatakamTraining Data Attribution Feature Learning
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Spectral Vision Transformer for Efficient Tokenization with Limited Data
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Efficient and Adaptive Human Activity Recognition via LLM Backbones
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FAME: Feature Activation Map Explanation on Image Classification and Face Recognition
May 12, 2026Xinyi Zhang, Manuel GüntherEtsi-Compliant Class Activation Map Image Classification
A Transfer Learning Evaluation of Deep Neural Networks for Image Classification
May 12, 2026Nermeen Abou Baker, Nico Zengeler, Uwe HandmannTransfer Learning Model Pretraining
One-Step Generative Modeling via Wasserstein Gradient Flows
May 12, 2026Jiaqi Han, Puheng Li, Qiushan Guo +3Discrete Diffusion Model Generative Models
Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network
May 11, 2026Md Fahimul Kabir Chowdhury, Jannatul FerdousBrain Tumor Classification Convolutional Neural Networks
Inducing Spatial Locality in Vision Transformers through the Training Protocol
May 11, 2026Eduardo Santiago Toledo, Asael Fabian MartínezVision Transformer Imagenet
USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation
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Counterfactual Stress Testing for Image Classification Models
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May 11, 2026Kaicong Huang, Weiheng Oh, Thomas Guggisberg +1Payment Networks Motion-Focused Representations
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May 11, 2026Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2Vision Transformer Self-Supervised Vision Transformers
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APEX: Audio Prototype EXplanations for Classification Tasks
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Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns
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May 15, 2026 · cs.LGJ/K move · Enter open · S save

Perforated Neural Networks for Keyword Spotting

Vishy Gopal, Aris Ilias Goutis, Ralph Crewe, Erin Yanacek+1

2Purdue University · 3Renesas Electronics · 1Perforated AI

Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and non-negotiable accuracy thresholds must all be satisfied simultaneously. Existing compression and optimization techniques can trade one resource for another, but rarely improve both accuracy and model size at the same time. This paper presents the application of Perforated Backpropagation to keyword spotting on the Edge Impulse platform, an experiment that won the Best Model award at the Edge Impulse 2025 Hackathon in December 2025. By adding artificial Dendrite Nodes to a standard convolutional neural network trained on the Edge Impulse keyword spotting tutorial pipeline, we demonstrate that dendritic models outperform traditional architectures at every level of parameter count and at every accuracy threshold tested across 800 hyperparameter trials. The best dendritic model achieved a test accuracy of 0.933 with only 1,500 parameters, versus the baseline accuracy of 0.921 requiring approximately 4,000 parameters. These results suggest that Perforated Backpropagation is a powerful addition to the edge AI engineer's toolkit, offering simultaneous gains in both model quality and deployment efficiency.

Heterogeneous Edge Devices Edge Convolutional Neural Networks Backpropagation

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