Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Developing efficient transfer learning strategies for robust scene
  recognition in mobile robotics using pre-trained convolutional neural
  networks   We present four different robust transfer learning and data augmentation
strategies for robust mobile scene recognition. By training three mobile-ready
(EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16,
ResNet50) convolutional neural network architectures on the widely available
Event8, Scene15, Stanford40, and MIT67 datasets, we show the generalization
ability of our transfer learning strategies. Furthermore, we tested the
robustness of our transfer learning strategies under viewpoint and lighting
changes using the KTH-Idol2 database. Also, the impact of inference
optimization techniques on the general performance and the robustness under
different transfer learning strategies is evaluated. Experimental results show
that when employing transfer learning, Fine-Tuning in combination with
extensive data augmentation improves the general accuracy and robustness in
mobile scene recognition. We achieved state-of-the-art results using various
baseline convolutional neural networks and showed the robustness against
lighting and viewpoint changes in challenging mobile robot place recognition.

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The categories of this paper are: cond-mat
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph