Dataset Culling: Towards Efficient Training of Distillation-Based Domain Specific Models
Published: Sep 1, 2019
Abstract
Real-time CNN-based object detection models for applications like surveillance can achieve high accuracy but are computationally expensive. Recent works have shown 10 to 100x reduction in computation cost for inference by using domain-specific networks. However, prior works have focused on inference only. If the domain model requires frequent retraining, training costs can pose a significant bottleneck. To address this, we propose Dataset...
Paper Details
Title
Dataset Culling: Towards Efficient Training of Distillation-Based Domain Specific Models
Published Date
Sep 1, 2019
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