title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ð¸ Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð²ÑÑÐ¾ÐºÐ¾ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Exploring the applicability of knowledge distillation methods to create high-performance neural networks
Creators: name: ÐÐ¸ÐºÐ¾Ð»Ð°ÐµÐ²Ð°, Ð­Ð»ÑÐ²Ð¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ñ Ð·Ð½Ð°Ð½Ð¸Ð¹
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ²Ð°Ð½ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹
neural network
knowledge distillation
deep learning
neural network quantization

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ð¸ Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð²ÑÑÐ¾ÐºÐ¾ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ñ Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ¶Ð°ÑÐ¸Ñ, Ð¼ÐµÑÐ¾Ð´Ð¾Ð², Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÐ¸Ñ ÑÐ¸ÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ ÑÑÑÑÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ ÑÑÐµÐ½Ð¸ÐºÐ°, Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², ÑÑÐ¸ÑÑÐ²Ð°ÑÑÐ¸Ñ ÑÑÑÑÐºÑÑÑÐ½ÑÐµ Ð·Ð½Ð°Ð½Ð¸Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¸ÑÐºÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ ÑÑÐµÐ½Ð¸ÐºÐ¾Ð² Ð² Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ð¸ Ð·Ð½Ð°Ð½Ð¸Ð¹. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð¾Ð±Ð·Ð¾Ñ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ð¸ Ð·Ð½Ð°Ð½Ð¸Ð¹. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ðµ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°Ð¼Ð¾ÑÑÐ¾ÑÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÑÐ¶Ð°ÑÐ¸Ñ Ð¸ Ð´Ð¸ÑÑÐ¸Ð»Ð»ÑÑÐ¸Ð¸ Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ Ð² ÑÐ°Ð¼ÐºÐ°Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð½Ð° ÑÑÐ°Ð½Ð´Ð°ÑÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ CIFAR-10 Ð¸ CIFAR-100. ÐÐ¾ Ð¸ÑÐ¾Ð³Ð°Ð¼ ÑÐµÑÑÐ¾Ð² ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½Ñ ÑÐ°Ð±Ð»Ð¸ÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð±ÑÑÑÑÐ¾Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð².
description: The goal of the work is to investigate the applicability of knowledge distillation methods to create high-performance neural networks. The object of the study is knowledge distillation of neural networks. In this paper, are reviewed the classical methods of compression, methods that use a fixed structure of the student neural network, and methods that take into account the structural knowledge in the search for student neural network architectures in the knowledge distillation. An analytical review of the reviewed knowledge distillation methods was also conducted. Experimental comparison of self-learning methods, classical compression and knowledge distillation methods for neural networks in the context of the image classification problem on the standard data sets CIFAR-10 and CIFAR-100 has been conducted. Based on the results of the tests, the tables comparing the accuracy and performance of neural networks obtained using the methods under consideration is compiled.