Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Pre-Trained Image Features and Synthetic Images for Deep Learning   Deep Learning methods usually require huge amounts of training data to
perform at their full potential, and often require expensive manual labeling.
Using synthetic images is therefore very attractive to train object detectors,
as the labeling comes for free, and several approaches have been proposed to
combine synthetic and real images for training.
  In this paper, we show that a simple trick is sufficient to train very
effectively modern object detectors with synthetic images only: We freeze the
layers responsible for feature extraction to generic layers pre-trained on real
images, and train only the remaining layers with plain OpenGL rendering. Our
experiments with very recent deep architectures for object recognition
(Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnet
and Resnet) show this simple approach performs surprisingly well.

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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat