We show that a compressed network can be created by starting with a model pre-trained for the task of visual place recognition and then fine-tuning it via ...
We show that a compressed net- work can be created by starting with a model pre-trained for the task of visual place recognition and then fine-tuning it via ...
We show that a compressed network can be created by starting with a model pre-trained for the task of visual place recognition and then fine-tuning it via ...
MOTIVATION. ○. Deep Neural network based place recognition models need a lot of memory. ○. But, a tourist takes a picture of a place and.
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Jun 5, 2020 · This paper provides a timely overview of both old and current compression techniques for deep neural networks, including pruning, quantization, tensor ...
Missing: Recognizing | Show results with:Recognizing
Model compression reduces a neural network without compromising accuracy. Learn about 4 model compression techniques.
Oct 10, 2024 · This paper investigates how deep multilayer perceptrons (MLPs) encode these feature manifolds and connects this behavior to the Information Bottleneck (IB) ...
Missing: Recognizing | Show results with:Recognizing
Feb 1, 2023 · Our study is intended to provide a first and preliminary guidance to choose the most suitable compression technique when there is the need to ...
Missing: Recognizing | Show results with:Recognizing
Jun 9, 2022 · In this paper, model compression of convolutional neural networks is constructed as a multiobjective optimization problem with two conflicting objectives.
To address these issues, this paper develops a deep neural network compression framework to reduce the resource usage for efficient visual inference. By ...
Missing: Recognizing Places.