대학원 인공지능학과 및 4단계 BK21 Ajou DREAM 인공지능 혁신인재양성사업단에서는
AI산업체 특강을 4월 5일(화) 오후 3시에 개최하고자 합니다.
관심있으시면 많은 참석 부탁드립니다.

* 주제 : Learning Features with Parameter-Free Layers
* 일시 : 2022년 4월 5일(화) 오후 3시~
* 강연자: 한동윤 박사(네이버AI랩)
* Zoom회의 참가
https://ajou-ac-kr.zoom.us/j/89312042426?pwd=SUFJWjlHUnZRRFM4c2lNRDl3V2FwZz09
회의 ID: 893 1204 2426
암호: 274506

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* Abstract
Trainable layers such as convolutional building blocks are the standard network design choices by learning parameters to capture the global context through successive spatial operations. When designing an efficient network, trainable layers such as the depthwise convolution is the source of efficiency in the number of parameters and FLOPs, but there was little improvement to the model speed in practice. This paper argues that simple built-in parameter-free operations can be a favorable alternative to the efficient trainable layers replacing spatial operations in a network architecture. We aim to break the stereotype of organizing the spatial operations of building blocks into trainable layers. Extensive experimental analyses based on layer-level studies with fully-trained models and neural architecture searches are provided to investigate whether parameter-free operations such as the max-pool are functional. The studies eventually give us a simple yet effective idea for redesigning network architectures, where the parameter-free operations are heavily used as the main building block without sacrificing the model accuracy as much. Experimental results on the ImageNet dataset demonstrate that the network architectures with parameter-free operations could enjoy the advantages of further efficiency in terms of model speed, the number of the parameters, and FLOPs. Code and ImageNet pretrained models are available at this https URL.
https://arxiv.org/abs/2202.02777