Search Results for author: Peter Beerel

Found 4 papers, 1 papers with code

Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types

no code implementations15 Sep 2023 Yue Hu, Gourav Datta, Kira Beerel, Peter Beerel

This implies that ML pipelines might not need explicit correction for RS for many object detection applications, but mitigating RS effects in ISP-less ML pipelines that target fine-grained location of the objects may need additional research.

object-detection Object Detection +1

FireFly A Synthetic Dataset for Ember Detection in Wildfire

1 code implementation6 Aug 2023 Yue Hu, Xinan Ye, Yifei Liu, Souvik Kundu, Gourav Datta, Srikar Mutnuri, Namo Asavisanu, Nora Ayanian, Konstantinos Psounis, Peter Beerel

This paper presents "FireFly", a synthetic dataset for ember detection created using Unreal Engine 4 (UE4), designed to overcome the current lack of ember-specific training resources.

object-detection Object Detection

Self-Attentive Pooling for Efficient Deep Learning

no code implementations16 Sep 2022 Fang Chen, Gourav Datta, Souvik Kundu, Peter Beerel

With the aggressive down-sampling of the activation maps in the initial layers (providing up to 22x reduction in memory consumption), our approach achieves 1. 43% higher test accuracy compared to SOTA techniques with iso-memory footprints.

Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation

no code implementations NeurIPS 2021 Souvik Kundu, Qirui Sun, Yao Fu, Massoud Pedram, Peter Beerel

Knowledge distillation (KD) has recently been identified as a method that can unintentionally leak private information regarding the details of a teacher model to an unauthorized student.

Knowledge Distillation

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