Network of Steel: Neural Font Style Transfer from Heavy Metal to
Corporate Logos
Aram Ter-Sarkisov
Artificial Intelligence Research Centre, CitAI, City, University of London, U.K.
Keywords:
Neural Font Style Transfer, Generative Networks.
Abstract:
We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a
VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style,
minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coeffi-
cients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first
step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy
metal and corporate logos are very different artistically, in the way they emphasize emotions and readability,
therefore training a model to fuse the two is an interesting problem.
1 INTRODUCTION
Recently there has been a large number of applica-
tions of convolutional neural networks (ConvNets) to
neural style transfer. VGG16 (Simonyan and Zisser-
man, 2014) was used to extract features from both
content and style images (Gatys et al., 2016) to trans-
fer style onto a randomly created image or the content
image. This approach was improved in (Zhang and
Dana, 2017) by adding upsampling layers and making
the network fully convolutional. A number of gener-
ative adversarial networks, GANs (Goodfellow et al.,
2014) were developed and successfully applied to the
neural style transfer for images and videos, such as
CycleGANs (Zhu et al., 2017), Pix2pix (Isola et al.,
2017), pose-guided GANs (Ma et al., 2017).
Font neural style transfer is an area of neural style
transfer that is concerned with the transfer and gener-
ation of font styles. In (Azadi et al., 2018) GAN was
developed that synthesizes unseen glyphs (characters)
given the previously observed ones in a particular dec-
orative style. In (Yang et al., 2019) GANs are trained
to transfer style (fire, water, smoke) to glyphs to cre-
ate an artistic representation. GlyphGAN (Hayashi
et al., 2019) was recently developed for generation
of glyphs in a required style. Neural font transfer
for logo generation (Atarsaikhan et al., 2018) uses a
framework similar to (Gatys et al., 2016), i.e. mini-
mizes distance to the style and content images by ex-
tracting features from ConvNet (VGG16) layers.
In this publication we would like to extend these
findings to a sparse case of logo style transfer: from
the style image, logo of a heavy metal band we want
to extract only foreground font ignoring the back-
ground. Content images are corporate logos. To the
best of our knowledge this is the first attempt to train
such a model. In Section 2 we introduce Network
of Steel that learns to transfer heavy metal logo style
while maintaining corporate logo structure, Section 3
presents the main results of the experiments, Section
4 concludes.
2 OUR APPROACH
We introduce Network of Steel that learns to trans-
fer the style from the heavy metal logo while main-
taining the structure of the corporate logo. We com-
pare two models, one based solely on VGG16 (Gatys
et al., 2016) and the other on Multistyle Generative
Network (Zhang and Dana, 2017). The advantage of
the former is that it does not require a large dataset
for training; instead, only one content and one style
image are used.
2.1 Heavy Metal and Corporate Logo
Style
In this publication we only concern ourselves with the
style of band logos, leaving out the style of album
covers, which is an entirely different area. Logos of
Ter-Sarkisov, A.
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos.
DOI: 10.5220/0009343906210629
In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), pages 621-629
ISBN: 978-989-758-397-1; ISSN: 2184-4313
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
621

heavy metal bands are usually carefully designed in
order to convey a certain group of emotions or mes-
sages, usually those of fear, despair, aggression, alert-
ness, eeriness, mystery. These logos are often a true
work of art. Several features stand out in many lo-
gos: the first and the last glyphs are more prominent,
often elongated and symmetric around the center of
the logo, most glyphs are decorated in the same style,
e.g. Megadeth logo glyphs have sharpened kinks at
the edges (arms), the first glyph (M) and the last glyph
(h) are about twice the size of other glyphs, with ex-
trusions and slanted bottom kinks. The logo is also
symmetric around the horizontal and vertical axes, see
Figure 1a.
On the other hand, corporate logos are often
barely distinguishable from plain text. Their design,
although often expensive in development, tends to be
functional, vapid and boring, with an emphasis on
readability and recognizability, see Figure 1b for Mi-
crosoft logo. This publications intends to bridge the
gap between the two by transferring the style from
the heavy metal band (Megadeth) to a corporate logo
(Microsoft).
Heavy metal band logos are an example of sparse
style in a sense that we only want to learn and trans-
fer font (glyph) features keeping the corporate logo’s
white background. This often leads to the creation of
a large number of artefacts, such as color pixels.
2.2 VGG16 and MSG Net
We compare two models, VGG16 (Simonyan and Zis-
serman, 2014) used by (Gatys et al., 2016) to transfer
style and multi-style generative network (Zhang and
Dana, 2017), which uses Siamese network to extract
features from the style image, fully convolutional net-
work (FCN) to extract features from the content im-
age and co-match layer to find correlation. VGG16
is presented in Figure 2 In this publication we re-
fer to the relevant Conv (convolution) layer using the
style adapted in most deep learning publications and
code,convi j , where i refers to the block in VGG16
and j to the Conv layer in that block. Block in VGG16
is an informal, but very useful term for our purpose.
The first two blocks have 2 Conv layers each, with
64 and 128 feature maps, equipped with ReLU and
MaxPool layers. The next three blocks have 3 Conv
layers each, also followed by ReLU and MaxPool lay-
ers, with 256, 512 and 512 feature maps.
2.3 Network of Steel
Following the framework of (Gatys et al., 2016), we
use exactly one style image, one content image and
one synthesized image that is updated every iteration
of the algorithm. Content and style features are ex-
tracted once before the start of the algorithm, and fea-
tures extracted from the synthesized image are com-
pared to them every iteration to obtain the loss value,
backpropagated it and update the synthesized image.
Our model, which we refer to as Network of Steel,
is VGG16 without the classifier layers (fc6,fc7).
Most generative networks use one last ReLU layer
from every block for style features extraction and the
second ReLU from the fourth block for content loss
(Gatys et al., 2016). Our first contribution is the use of
Conv layers for feature extraction and loss computa-
tion, because ReLU layers produce multiple artefacts.
Our second contribution is the use of coarse features
from the first block (conv1 1, conv1 2).
We show that extracting coarse features from the
style image minimizes artefacts in the stylized logo
and it is sufficient to use only two deep layers in ad-
dition to a coarse layer to transfer style without dis-
torting the corporate logo or creating many artefacts.
Finally, our third contribution are the layerwise loss
weights that determine the contribution of the layer
to the style loss. We show that VGG16 outperforms
MSG Net for style transfer.
The specific challenge that Network of Steel faces
is the sparse distribution of style features: the network
only needs to learn to transfer font features (shapes,
ornaments, glyph sizes and ratio, overall symmetry)
to the synthesized image and keep the background
neutral.
To find the content loss, we use the same layer
as in (Gatys et al., 2016), conv4 2, and minimize
mean squared error function between the features of
the content and the synthesized image. Each layer-
wise style loss is multiplied by the predefined loss co-
efficient; if the coefficient is different from 0, we refer
to the corresponding layer as an active layer:
L
Total
= L
Content
+
L
∑
l=0
c
l
E
l
(1)
Coefficients c
l
are layerwise loss coefficients speci-
fied in Section 3 and Appendix. For example, for
layer conv3 3 layer loss coefficient is c
3 3
. For style
and transferred image we compute correlation matri-
ces A
l
and G
l
for every active layer. Each element of
G
l
, G
l
i j
is a dot-product of vectorized feature maps in
that layer:
G
l
i j
= f
T
i
· f
j
(2)
This array is known as Gram matrix. Distance be-
tween Gram matrices for the style and synthesized
images, A
l
and G
l
is a contribution to the style loss,
ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods
622

(a) Megadeth logo, black and white
(b) Microsoft logo, black and white
Figure 1: Examples of heavy metal and corporate logos.
Figure 2: VGG16 architecture. There are in total five blocks, the first two blocks have two Conv layers, each followed by
ReLU and MaxPool layers, the last three have three Conv layers, each followed by ReLU and MaxPool layers. Image taken
from (Das et al., 2018).
and it is measured using mean squared error:
E
l
=
1
4H
2
W
2
C
2
∑
i, j
(A
l
i j
− G
l
i j
)
2
(3)
Equation 1 is different from the total loss equation in
(Gatys et al., 2016) because we only use layer-specific
style loss coefficients, and do not use the style loss co-
efficient. Here H is the height of the feature map, L is
the length of the feature maps and C is the number of
channels/feature maps in layer l. We set most layer-
wise coefficients to 0, and establish which active lay-
ers contribute most to the creation of a readable cor-
porate logo with the best heavy metal style features
and least artefacts, which is the main contribution of
this publication.
3 EXPERIMENTS
We compare the results of Network of Steel, MSG
Net and VGG16 as implemented in the GitHub repos-
itory https://github.com/rrmina/neural-style-pytorch.
VGG16 uses Conv layers conv1 2, conv2 2,
conv3 3, conv4 3, conv5 3.
Weights in VGG16 which extracts features from
the synthesized image are frozen for the full duration
of the algorithm, and only partial derivatives for the
pixels x in the synthesized image are computed and
updated:
∂L
Total
∂x
6= 0.
Network of Steel is always initialized with the
content image and pretrained VGG16 weights. Only
specified layers contribute to the style loss. For train-
ing we use Adam optimizer with a learning rate of 1.0
and regularization constant of 0.001. We use the same
MSG-Net hyperparameters as in the GitHub project:
https://github.com/zhanghang1989/MSG-Net.
We select three values for layerwise coefficients:
0, 20, 200, 2000. If the coefficient is set to 0, the style
loss from this layer is not computed and ignored dur-
ing training. Only losses from the active layers con-
tribute to the total loss. To test our ideas, we tried
three different groups of layers to compute style loss:
1. Two layers, the fist one is always conv1 2, the
other layer is the last Conv layer from one of the
last three blocks.
2. Three layers, the fist one is always conv1 2, the
other two layers are the two last Conv layers from
one of the last three blocks.
3. Two blocks, the fist one is always the first block
of VGG16, the other one is one of the last three
blocks.
Obviously far more combinations of layers and
loss coefficients can be tested, but empirically we de-
termined that adding more layers and increasing loss
coefficients leads to the deterioration of the network’s
performance, so these results are not presented.
3.1 Baseline VGG16 Model
Results for the generative network from (Gatys et al.,
2016) are presented in Table 1. In the original pub-
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
623

lication and code the last ReLU layer of each block
is used with different weights, but most open-source
implementations use total style loss of 1e2 and lay-
erwise loss coefficient of 0.2. We use a higher style
loss weight = 1e5. In addition to 0.2 we try coeffi-
cients of 0.02 and 0.002. For the lowest loss coeffi-
cent, the model displays artefacts (color pixels). For
higher coefficients it distorts the font and makes the
logo unreadable. This demonstrates that the selection
of layers for loss computation alone is not enough for
sparse style transfer, and adjusting loss coefficients
affects the presence of artefacts and degree of style
transfer.
3.2 Two Style Layers
Content features include the structure (text) of the cor-
porate logo, but learning these features always leads
to a large number of artefacts in the transferred image.
To minimize these artefacts, the second conv layer
from the first block, conv1 2 is used. This coarse
layer is responsible for the learning of color features
from the style logo, which transfers the background
features (white color), but does not learn font features
like the edgy shapes of the glyphs in Megadeth logo.
Using only one other layer from the last three blocks
increases both the effect of style glyph features, in-
cluding the elongation of the first and the last glyphs
(elongated bottom of the front ‘leg’ of M and t, as
particularly obvious in Tables 3 and 4, and the pres-
ence of the artefacts: font decay, colored pixels in the
background, wrong style features (like a vertical ‘ex-
tension’ to glyph ‘o’ in Table 4).
Increasing loss coefficient for the deep layers al-
ways leads to the transfer of more subtle features,
like the more ‘rectangular’ shape of the intermediate
glyphs (all glyphs except the first and the last one).
More sophisticated style features, like small horizon-
tal extrusions on top of ‘legs’ of M and t remain a
challenge with two style layers. Either the model
evolves small black vertical ‘blob’, like in Table 2,
or merges this extrusion with the dot over i, the next
glyph. For the same glyph M, the elongated bottom
of the front leg is a lesser challenge, (see Table 4 with
conv3 3, to a lesser extent this is achieved in Table 3
with conv4 3).
It is noticeable that the last layer in the network,
conv5 3 contributes very little to the style transfer.
We explain this by the size of the layer and the size
of the loss it generates. conv4 3 and conv3 3 have
either the same (512) or fewer (256) number of maps,
but they are larger. Convergence of the networks
with the largest coefficients, conv1 2 = conv5 3 =
conv4 3 = conv3 3=2000, is shown in Figure 3a. For
the same size of the loss coefficient, conv3 3’s con-
tribution to the style loss is much higher than of the
other layers.
3.3 Three Style Layers
The results for the fifth block are very similar to the
previous case with two style layers: the network fails
to learn any significant style features. Same is true for
the fourth block, only when the largest loss coefficient
is used it manages to evolve some style features, like
the elongation of the last glyph. The third block learns
the style very aggressively, and for c
3 2
=c
3 3
=200
most style features are learnt, including the horizon-
tal kinks on top of the first glyph, but at the cost of
overall deterioration of the logo quality, see Table 7.
This is reflected in the convergence of the net-
works, see Figure 3b. Despite some progress com-
pared to the networks with two layers, there seems to
be a lack of good tradeoff between learning of style
and maintaining the structure of the corporate logo.
This is reflected in the addition of another coarse layer
in the next subsection.
3.4 Two Style Blocks
With two blocks, we use all the Conv layers in each
block. Empirically we established that adding even
more layers or whole blocks either does not improve
the result, or the logo readability starts to deteriorate.
To find a better tradeoff between readability and
style transfer, we add another coarse layer, conv1 1,
so the total number of active layers increased to five,
same as in (Gatys et al., 2016). This effect explains
why some results have fewer style features than in
the previous subsection, despite adding another deep
layer.
The overall result for the fifth block in Table 8 is
still quite weak, but with the largest loss coefficients
for all layers it manages to transfer some style for the
first and the last glyphs, and the intermediary glyphs,
with very few artefacts. The third block contributes
much more to the style loss, so the best results is
achieved for c
1 1
=c
1 2
=2000 and c
3 1
=c
3 2
=c
3 3
=20,
but further increase of style loss coefficients leads to
the deterioration of the transferred logo. Neverthe-
less, for the largest loss coefficients the network al-
most correctly evolves the first and the last glyphs
at the cost of adding few background artefacts and
slight deterioration of the readability of the synthe-
sized logo.
The best results that balance the heavy metal style
and corporate logo readability were obtained using
the fourth block with the style loss coefficients of 200
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624

and c
1 1
=c
1 2
= 2000 in Table 9: the model evolved
most of the challenging features of the metal logo
without a heavy presence of artefacts. They are an
improvement over the results in Table 6 in most ways,
which proves that adding both a deep style layer and
a coarse layer to maintain the content font structure
improves the final result. This could be seen in Figure
3c: the third block generates more style loss than the
other two blocks, but produces an overall worse result
than the fourth block that manages to maintain better
readability.
3.5 MSG Net
MSG Net was introduced in (Zhang and Dana,
2017).We finetuned it to our data that we scraped
from the internet: 19 corporate logos (content) and
11 heavy metal logos (style). Style loss hyperparam-
eter was set to 10000, content loss hyperparameter to
1, learning rate to 1.0.
Although MSG Net is more advanced than plain
VGG16: it has a fully convolutional architecture,
learns weights to evolve an image with the transferred
style and has more loss functions, it performs worse
than Network of Steel in terms of sparse style transfer,
as it does not transfer any font style from heavy metal
logos onto the font in the corporate logos at all. MSG-
Net manages to evolve some small elements around
the glyphs, that are barely noticeable.
4 CONCLUSIONS
Sparse style transfer requires an approach different to
that of other neural style transfer problems, due to a
large number of artefacts, merging and distortion of
elements of the style and font.
In this publication we introduced Network of Steel
for sparse style transfer from heavy metal band to cor-
porate logos. We showed that in order to synthesize
a readable logo with heavy metal style elements, in-
stead of using layers from all blocks of VGG16, only
one or two coarse layers and two or three deep layers
are enough. Our future work includes the following
challenges:
1. Train a separate network for loss coefficients,
2. Build a large database for training Networks of
Steel for different heavy metal styles and corpo-
rate logos,
3. Design accuracy metrics applicable to this prob-
lem to enable visual comparison,
4. In this paper we only used a white background for
heavy metal logos, which causes a lot of artefacts.
In the future we will use different, more challeng-
ing backgrounds, like album covers.
We showed that conv1 2 is essential to maintaining
artefact-free background and layers from the third
block in VGG16 learn style faster than deeper lay-
ers like conv5 3 and conv4 3. Our approach is sim-
ple and more robust than (Gatys et al., 2016) and
(Zhang and Dana, 2017) for sparse style transfer.
The whole deep fourth block (conv4 1, conv4 2,
conv4 3) with loss coefficients of 200 and two coarse
layers (conv1 1 and conv1 2) with loss coefficients
of 2000 produce the best tradeoff between heavy
metal style and the readability of the corporate logo.
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APPENDIX
Here we present some of the results that demonstrate
the effect of different layer loss coefficients for logo
style transfer. In each experiment, all excluded (in-
active) layer loss coefficients are set to 0. All exper-
iments were run for 50000 iterations with the same
learning rate and content loss weight of 1, network
gradients switched off. Due to the differences in the
architecture and training, MSG-Net was evaluated on
a number of heavy metal and corporate logos, the best
results presented in Table 11.
Table 1: Results for VGG16 model, layers conv1 2, conv2 2, conv3 3, conv4 3, conv5 3, as defined in (Gatys et al., 2016).
c
1 2
=c
2 2
=c
3 3
=c
4 3
=c
5 3
=20 c
1 2
=c
2 2
=c
3 3
=c
4 3
=c
5 3
=200 c
1 2
=c
2 2
=c
3 3
=c
4 3
=c
5 3
=2000
Table 2: Results for layers conv1 2 and conv5 3.
c
5 3
=20 c
5 3
=200 c
5 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
Table 3: Results for layers conv1 2 and conv4 3.
c
4 3
=20 c
4 3
=200 c
4 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
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626

Table 4: Results for layers conv1 2 and conv3 3.
c
3 3
=20 c
3 3
=200 c
3 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
Table 5: Results for layers conv1 2 and conv5 2, conv5 3.
c
5 2
=c
5 3
=20 c
5 2
=c
5 3
=200 c
5 2
=c
5 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
Table 6: Results for layers conv1 2 and conv4 2, conv4 3.
c
4 2
=c
4 3
=20 c
4 2
=c
4 3
=200 c
4 2
=c
4 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
627

Table 7: Results for layers conv1 2 and conv3 2, conv3 3.
c
3 2
=c
3 3
=20 c
3 2
=c
3 3
=200 c
3 2
=c
3 3
=2000
c
1 2
=0
c
1 2
=20
c
1 2
=200
c
1 2
=2000
Table 8: Results for the first and the fifth block: conv1 1 conv1 2, and conv5 1, conv5 2, conv5 3.
c
5 1
=c
5 2
=c
5 3
=20 c
5 1
=c
5 2
=c
5 3
=200 c
5 1
=c
5 2
=c
5 3
=2000
c
1 1
=c
1 2
=0
c
1 1
=c
1 2
=20
c
1 1
=c
1 2
=200
c
1 1
=c
1 2
=2000
Table 9: Results for the first and the fourth block: conv1 1 conv1 2, and conv4 1, conv4 2, conv4 3.
c
4 1
=c
4 2
=c
4 3
=20 c
4 1
=c
4 2
=c
4 3
=200 c
4 1
=c
4 2
=c
4 3
=2000
c
1 1
=c
1 2
=0
c
1 1
=c
1 2
=20
c
1 1
=c
1 2
=200
c
1 1
=c
1 2
=2000
(a) Two style layers (b) Three style layers (c) Two style blocks
Figure 3: Style loss for each network with all loss coefficients = 2000 plotted against thousands of iterations. Continuous
curve: fifth block layers, dashed curve: fourth block layers, dotted curve: third block layers.
ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods
628

Table 10: Results for the first and the third block: conv1 1 conv1 2, and conv3 1, conv3 2, conv3 3.
c
3 1
=c
3 2
=c
3 3
=20 c
3 1
=c
3 2
=c
3 3
=200 c
3 1
=c
3 2
=c
3 3
=2000
c
1 1
=c
1 2
=0
c
1 1
=c
1 2
=20
c
1 1
=c
1 2
=200
c
1 1
=c
1 2
=2000
Table 11: Results for MSG-Net model.
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos
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