GANet: Glyph-Attention Network for Few-Shot Font Generation
Authors: Mingtao Guo
Wei Xiong
Zheng Wang
Yong Tang
Ting Wu
Venue: ICLR 2022 Submitted (2022)
Keywords: font generation
GANet
glyph-attention
few-shot
GAN

Abstract: Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles. The time and cost of such task will be greatly reduced if the complete font library can be generated from only a few custom samples. Inspired by font characteristics and global and local attention mechanism Wang et al. (2018), we propose a glyph-attention network (GANet) to tackle this problem. Firstly, a content encoder and a style encoder are trained to extract features as keys and values from a content glyph set and a style glyph set, respectively. Secondly, a query vector generated from a single glyph sample by the query encoder is applied to draw out proper features from the content and style (key, value) pairs via glyph-attention modules. Next, a decoder is used to recover a glyph from the queried features. Lastly, Adversarial losses Goodfellow et al. (2014) with multi-task glyph discriminator are employed to stablize the training process. Experimental results demonstrate that our method is able to create robust results with superior fidelity. Less number of samples are needed and better performance is achieved when compared to the other state-of-the-art few-shot font generation methods, without utilizing supervision on locality such as component, skeleton, or strokes, etc.

Under review as a conference paper at ICLR 2022
GANet: Clyph-Atttnnnnnnnnnn Network for Few-Shot Font Gen- cration
Anonymous authors Paper under double-blind review
Abstract
Font generation is valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles. The time and cost of such task will be greatly reduced if the complete font library can be generated from only a few custom samples, Inspired by, font font characteristics and global and local attention mechanismWang et al. (2018), we propose a slyph-attention network (GANet) to tackle this prob- lem. Firstly, content encoder and style encoder are trained to extract features as keys and values from a content glyph set and a style glyph set, respectively. Secondly, a query vector generated from a single glyph sample by the query encoder is applied to draw out proper features from the content and style (key, value) pairs via glyph-attention modules. Next, a decoder is used to recover glyph from the queried features. Lastly, Adversarial lossesGoodfeelovv et al. (2014) with multi-task glyph discriminator are em- ployed to stablize the training process. Experimental results demonstrate that our method is able to create robust results with superior fidelity. Less number of samples are needed and better performance is achieved when compared to the other state-of-the-ar few-shot font generation methods, without utilizing supervision on locality such component, skeleton, or strokes, elc.
Introduction
With the increasing popularity of Chinese characters globally, the demand for Chinese fonts is rising. However, it's very time consuming and expensive to design such font library. As we know, the number of letters in English alphabet 52 (upper and lower cases), much smaller than that of the Chinese characters which is over 20,000. It takes about 6 months for an experienced designer to design a Chinose font library. Typically, the designer starts the font design with manually designing few hundred of Chinese characters which encompass most of the radicals (i.e., the components of the characters). The rest of the characters will be completed by mixing and matching the radicals based on word vocabulary. However, it is very tedious and time consuming in both the initial design phase and the block building process. If only a few Chinese glyphs(e.g, 4 or 8) are needed by manual design and there is a tool that can automatically complete the font library of the 20,000+ characters, designers' efficiency will be greatly improved.
Recently, image to image translation based methods Jiang et al. (2019) have been proposed to generate fonts antomatically. Most of these approaches are based on generative adver- sarial network Goodfellow et al (2014). which was shown to be able to transfer one font style another successfully. However, they need large scale paired or un-paired source and target domain dataset to train model. It's inefficient because for every new font style, the pretrained model needs to be finetuned. Moreover, it is difficult and expensive to collect target font dataset such Chinese historical calligraphic works, whose font glyphs may just have a small percentage loft. But these methods typically require hundreds of font glyphs for training as minimum.
In order to address the limitation of lack of dataset, several few-shot font generation methods have been developed Gao et al. (2019)ark et al. (2021), which don't demand lot of glyphs

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to train the model. They are capable of producing a complete font library with just few samples. Although these methods can fuse source content and target style representations successfully with few font glyphs unseen style during training, they tend to output characters with either wrong content or weak style.
The unsatisfactory results of recent few-shot font generation methods make re-cxamine the style and content relationship in the glyphs. Chinese glyph can be divided into two parts (i.e. content and style). From the content perspective, we recognize a glyph's meaning at a glance. It implies that human understands font's content through vision based on font global information. Inspired by this observation, we propose content glyph-attention module to capture the global features from the content set of glyphs. On the other side, special stroke and radical details of glyph determine the its style. Tn other words, glyph style is closely related to local information. Based on this hypothesis, we propose a style glyph-attention module to encapsulate pattern information from the style of glyphs.
Our contributions are summarized as follows:
We propose a generic few-shot font generation framework that bridges the gap between font content and style.
We propose a content and style glyph-attention modules to characterize overall structure information from the content set and local stroke details from the style set, respectively.
Experiments demonstrate superior performance in terms of both quantitative and qualitative results compared with other cutting-edge few-shot glyph generation methods.
Related Work
2.1 Style Transfer
Few-shot Chinese font generation aims to combine the content and reference style. It is special style transfer problem in essence. However, font design requires high accuracy in content structure, the achulresult.go traditional style transfer model for font generation is nsnally unsatisfactory. Gatys al. (2016) proposed neural style transfer in VGG Simonyan & Zisserman (2014) leat speed. Johnson et al. (2016) trained fast neural style transfer(FNST) model, which able to merge style and content representations in real-time. Although speed-up is achieved in FNST, a new hwwpstyyle.aee image is necessary to retrain the model for transfer. In order to solve this problem, Dumoulin et al, (2016) proposed n-style transfer method which can embed style by using conditional instance normalization. Further improvement was made by Huang & Belongic (2017) by inventing AdaIN that can achieve arbitrary style transfer in real time without retraining à model.
by reconstructing the gram matrix but with limited inference
2.2 Image to Image Translation
Although style transfer method has advantage in combining content and style represen- tations, it may damage the content structure. For font generation, it is crucial to keep character semantics so that they are dissuallydistiiggushabbb
and recognizable. Image to image Iranslation method (pix2pix) [sola et al (2017) was devised to transfer from the source domain to the target domain (e.g., from a sketch to a real cat drawing). Pix2pix dif- fers from style transfer based methods in that it prevents the structure from being destroyed. Nevertheless, the result quality of pix2pix is heavily dependent, on paired dataset that expensive to collect. In order to tackle this problem, Zhu et al. (2017) moved a step fur- ther by introducing unpaired image to image translation method method (CycleGAN), which maps the source domain to the target domain by using cyclic loss. Although CycleGAN made theak-through by climinating the necessity of paired dataset, it's inefficient because different (source, target) domain tuples require different models for inference. Huang et al (2018) extended CycleGAN by implementing multi- transfer a single model. Recently, Liu et. al. (2019) put forward few-shot image to image translation using AdaIN.
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2.3 Attention Models
Lately, attention mechanisms are widely adopted in several models in order capture global dependencies Xu et al (2015). In particular, self-attention (also known as intra-attention) calculates the response at a, position in sequence by attending to all positions within the same sequence Parikh et al. (2016). (SAGAN) Zhang al (2019) demonstrated thaf attention could improve image generation quality the GAN model. (non-local) Wang et al. (2018) used reformalized self-attention module in spatial-temporal domain between video sequences. To our knowledge, attention module has not yet been explored in font generation tasks. In this study, apply content and style glyph attention modules to efficiently capture the characteristics the font content and style sets.
2.4 Few-shot Font Generation Methods
The goal of few-shot font generation method is to make the generated glyphs akin to the style references, which are only few samples, without ro-training a new_mmeode model At the
same time. the generated glyphs' semantic is unchanged. Recently, Zhang et al. (2018) proposed EMD model that leverages a pair of encoders merge content and style. Gao el al. (2019) invented AGIS-Net to reach the few-shot font generalion goal by transferring boothssaape shape and lexture with a few reference samples. Unlike other methods, MX-Font Park et al (2021) extracts multiple style features not explicitly conditioned component labels, but automatically by multiple experts to represent different local concepts.
METHOD
As mentioned above, our goal is generate stylized glyph images from small number of reference samples. We design three encoders, two glyph attention modules and one decoder to form Glyph-Attention Network(GANet). Details of the model architecture and loss functions are discussed in Sections 3.1 and 3.2.
Similar Style
Ground Truth
+
Style
Style Encoder
Real?
Style Glyph
Discriminator
kg
Style
Query Encoder
Decoder
Synthesized
kg
Content Glyph
Content Encoder
Attention
-
Content Discriminator
Real?
Ground Truth
Content Set
Same Content
Figure 1: The framework of proposed few-shot font generation model GANet. A query encoder is used to extract glyph content from the content encoder and glyph style from the style encoder by two glyph-attention modules. They are subsequently added element-wise before being fed into the decoder. Two discriminators are employed stablize the training process. For more details, please refer to the appendices.
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3.1 Network architecture
As shown in Figure [ GANet consists of one query encoder, one style encoder, one content encoder, two glyph-altention modules and one decoder. In order to stablize the training process and improve the synthesized results quality, two discriminators are adopted.
3.1.1 Query, Content and Style Encoders
We formulate the content, style and query encoding process as trainable high-dimensional glyph feature extractors. The content encoder Eee aims to extract semantic feature. The imput of the content encoder is X = {01, 22,,,,, TNy, each of them has the same content but different style, the output of the content encoder is F. (X)))) E RN Similarly, the style encoder Eg is used to characterize style features from the the style set X, = fy1 V2,..., "/N' , which contains N glyphs with the same style but different content. The output of the style encoder is F, = (X,).) E x The query encoder E, is devised to oblain query feature vector F. that is essential for the glyph-attention modules to identify local style features from the style feature set F3 and generate the most proper global content feature from the content feature glyph image X, whose content is the same as the content in the content glyph set but with a new style. Output of the query encoder is Fg - (X,,,) ) E IR #xxx The three encoders E,, Eg and Ec have identical architecture but they do not share weights. Symbols H, W and C' represent feature map's height, width and channel respectively.
Fe. The input of the query encoder
3.1.2 Decoder
The style glyph-attention module intended to query a local style feature tensor from the style feature set. Similarly, the objective of the content glyph-attention module is to acquire the most proper glyph global content from the content feature set. The outputs of the content, style, and query encoders added clement-wise before being fed into the
decoder. As defined in ①1)
F SGA(g. k. U2) + KG ve) +
(1)
O = Decoder(F))
(2)
Where SGA and CGA are abbreviations of Style and Content Glyph-Attentee respectively, ks and Us are equal F. which is from the style encoder. Similar SGA, Ko and ve are equal to Fo which is from the content encoder. q is equal to Fg which is from the query encoder. O is the synthesized result generated by the decoder.
3.1.3 Multi-task discriminators
has been demonstrated that generative adversarial networks Goodfellow et al, (2014) are able to generate output with distribution close to that the actual data. There- fore, leverage GAN to develop model that outputs synthesized glyph images whose content and style comply the distributions of the input content and reference style. In principle, conventional discriminator, which can discern the fake and real data, can per- form the discrimination task independently to complete the generation task. However, the synthesized results are not satisfactory because they are often wrong in content or weak in style. To enhance the model performance, multi-task discrininators are proposed to further disentangle content and style information. They are. cawled çontent D. and style D, discrim- inators respectively, based on SNGAN Miyato & Koyama (2018). which uses trainable embedding matrix to obtain conditional information. D, allows closer content distributions between the model output and the real data, and D, helps the convergence of the output style to that of the real data.
3.2 Glyph allention
Most few-shot immge generation methodsLiu al. (2019) adaptive instance normaliza- tion (AdaIn) Huang & Belongie (2017) to transfer image style such as texture and color to another which provide content like pose and structure. However, it's challenging to transfer

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font style another due to the fact that font does not have texture and color, just con- sists of some white and black strokes. Different from AdaIn, we propose style and content glyph-attention modules to extract style and content features from custom glyph sets. The proposed method Glyph-Attention Network (GANet) is named after the glyph-attention modules, which are shown in Figure 2
3.2.1 Style glyph-attention
As we all know, font style mainly depend on local stroke details, so it means that local features of glyph dictate styles. Based on this hypothesis, we propose the style glyph- attention module. The style encoder provides us style feature set F, E IR N which contains the desired font style. A style glyph-attention module is utilized to query the style feature from the style set. vs and k, denote the value and key in the attention module. They are equal to Ez. The query vector ##xxxC is derived from the query encoder. Key ke and query q are first transformed into two feature spaces far 9a. where f.(9) W,.9. (o(ks) W kg. We then reshape gs(k:) NNW xC and transposc and reshape fe(1) E RCxHW, Finally, they used to calculate the attention map B, as defined in (3)
Style Set
Query
Content Set
Style Encoder
Query Encoder
Content Encoder
q
NotroWWc
kg
VE
Reshape
Reshape
Reshape InniClu
Reshape
Style Glyph Attention
Oumtiisc
Cx/ott)
+e/oman
Content Glyph Attention
Netrww
softmax
softmax
CajnWW
Reshape
Reshape
Figure 2: Glyph attention and content attention
exp(Eng)
By NHW erp(Smj)"
= (k.(aaa))a()))) & NHW
(3)
The attention map Bi indicates the extent to which the model attends to the ith style location when synthesizing jth Where H. W, C are the height, width and channel of features from previous 1 x 1 conv layer. N the number of glyphs in the style set. The output of the style glyph-attention layer is O, (O1, O2, ..., Oz Onw), O₂ E RIxC where,
NHW Oij = Σ
Tim Bajj r=ha(1) TER CxNNN
(4)
m=11
where T transposed and reshaped from (v, = Wh, v.. In addition, W.. E RCC W,, E CCC and Wg, E CxC are the learned weights in the conv layer of the style glyph-attention module. The final output is reshaped to O2 Hx
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3.2.2 Content glyph-attention
Different from the style glyph-attention module, which queries the style features from the local spatial area, the content glyph-attention mainly queries content features globally. It based on an assumption that content information resides in the overall structure of the skeletons. We define content feature set F. E I xxxxxxxxx that is extracted by the content encoder from the content set X, {I1, I...... IN Ve and ke denote the value and key in the attention module, which are equal to F.. The query vector q E H xC is identical to that the style glyph-attention module. Similarly, key k, and query q are transformed into two feature spaces fer Ge, where fe(g) W go(ki) = Waske- go(ke) RN and felg) E #xC1 are then reshaped to calculate the attention map 7, As defined in exp(es)
- (e(e(()))c( EERNKI
N Em exp((m))
The attention map % indicates the extent to which the model attends to the 8th global content set location. H, W, and C are the height, width and channel of features from previous 1 x conv layer. N is the number of glyphs in the content set. The output of the content glyph-attention layer O₂ (O1, Oz.... O5 OS...,,... OHwe), OF E R.
N
0, = Σ
Tmjfim:
I = hc(ve) TERNXHWC
(6)
m=1
where r is reshaped from (v(v)) = Wn. to- Co- In addition, Whe E RCx, Ws. E RCxC and W,, a. E RCC are the learned weights in the x 1 conv layer of the content glyph-attention module. The final output is reshaped to Og E Hxxxx
3.3 Loss function
Three individual loss functions are combined during model training: identity loss, feature matching loss, and multi-tas task adversarial loss.
3.3.1 Identity loss
In order to make the output distribution of the model agreeing to that of the target in the pixel and feature space, we employ the identity loss. As we know, perceptual loss Johnson et al. (2016) is effective way measure the distance between images in VGG feature space. Compared to pixcl-wisc loss functions such as Mcan-Squarod Error(MSE) perceptual loss can recover more high-frequency details. However, every coin has two sides, due the downsampling operation (max-pooling) in VGG19, the perceptual loss function will result in partial information loss during reconstruction, especially low-fre www-frqquencis. To avoid losing low-freet wwefregnency details, we add L1 loss as complementary term with perceptual loss, as defined in (7)
(7) Where _3_1 is the feature map of VGG19 in relu3 1. X. and X, are input content set and reference style respectively, X, is the query image whose content the same the content set. T is the larget image whose content is the same as X,, with style similar to X, G the generator that consists of a content encoder, a query encoder, style encoder, decoder and two glyph-attention modules as shown in Figure
Lid 1 (T) D3 (G(Xe, Xgx Xxx))))11 + IT - Xx.
3.3.2 Feature matching loss
To stabilize the adversarial training, feature matching (FM) loss Salimans et al. (2016) also applied in this study. Similar to the perceptual similarity mcasure, the FM loss uses discriminator network the feature extractor, which contain large amount of information about the glyph images. To obtain more disentangled representations in content and style, we use D. and D, to extract feature maps, as defined in
Lfm= Σ (T) (G(Xa Xy. x.))| + ΣΣ
D (T) - (G X,. x.))||
(8)
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Where D is the feature map of layer i in the discriminator. We use all layers except fully-connected layer to extract features.
3.3.3 Multi-task adversarial loss
In the proposed framework, two condition discriminators De and D, are used improve model performance, each of which has its own mission. D. and D, aim at calibrating the model to allain accurate content and stronger style respectively. We utilize hinge version loss Lim & Ye (2177) as the adversarial objective function, as defined in (9-10),
LG xxx - (C
(9)
LD m00 (0,1 D (x))) + max (?)
(10)
maz D. Xg. X,))) + max(0,1 DT))))
3.3.4
Total loss
simple additive form is used for the total loss, as illustrated in (11-12),
Lo(G) = An Lis + A2Tafm + La
(11)
(D) = I.D
(12)
Where Å₁ and A are the hyper parameters of the generator loss function, 0 and are the weights for the generator and discriminators respectively.
Experiments
4.1 Experimental settings
We use the Adam optimizer Kingma & Ba (2014) to optimize all models with hyperparam-
eters 31 0 and 32 0.9. Using the two-time scale learning rates Heusel et al. (2017) strategy, we set the learning rate of all discriminators to 4e - 4 and the generalor to le - 4. The number of both X. and X, is set to and the size of all images is 128 x 128 x 3. The weights in the loss function are chosen to be A1 = 1 and Ag = 10. We train the model with batch size of 4 for 500,000 iterations in a Nvidia GTX 1080Ti GPU with 12G memory.
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Figure 3: Comparison of the results in 6 different font styles for each method

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Table Intersection Over Union (TOU) for many-shot and few-shot font generation methods
Intersection Over Union
Style
Type
Method
2
3
5
6
Mean
zi2zi
0.529
0.534
0.599
0.695
0.515
0.423
0.549
many shot
pix2pix
0.590
0.652
0.695
0.807
0.616
0.537
0.650
CycleGAN
0.206
0.272
0.262
0,465
0.325
0,312
0.307
ZiGAN
0.600
0.664
0.702
0.812
0.623
0.550
0.659
FUNIT
0.355
0.448
0.327
0.571
0.366
0.289
0.393
few
AGIS-Net
0.387
0.369
0.418
0.736
0.483
0.388
0.464
shot
MX-Font
0.260
0.362
0.382
0.648
0.490
0.295
0.406
GANet
0.711
0.683
0.749
0.778
0.481
0.401
0.634
Content
Style
1.00
FUNIT
RUNIT
AGIS- Net MX-Font
ADIS Net
0.95
0.85
MX-Fort
GANet
GANet
0.50
Ground truth
Top-1 accuracy
0.80
Ground truth
0.85
0.75
0.80
0.70
0.75
0.65
0.70
0.60
test
Figure 4: Top-1 accuracy of content and style in 6 different font styles for 4 few-shot font. generation methods
4.2 Datasets and evaluation metries
We collected 411 Chinese fonts conforming to the GB2312 standard. Each font contains 6,763 Chinese characters. We randomly selected 405 fonts as the training set and the remaining fonts as the test set. Various metrics are used to evaluate the quality of the synthesized glyphs. To mcasure the similarity between the generated image and the target, intersection over minnn(IOU) utilized. We further employ two classifiers with Inception-v3 Szegedy et al. (2016) backbone to distinguish between content and style labels in the test set.
4.3 Benchmarking
We compare our method, with four many-shot font font generation methods (i.c. pix2pix Isola et al. (2017), CycleGAN Zhu et al. (2017), ZiGAN Wen et al three slate-of-the-art lew-shot font generation generation.mel.nn meLhods fie., FUNIT Liu et al. (2019), MX-
zi2zi
Tian
(2021)
and
Font Park et al. (2021) and AGIS-Net Gao et al. (2019)). The four many-shol methods demand lot of training data and different model is required for each font style during inference. As such, for many-shot methods, we further split each test font library into 5763 glyphs for training and 1000 glyphs for testing. Therefore, each of the four many- shot methods generates 6 models. In comparison, few-shot methods including the proposed method GANet need only a few glyphs to transfer font style during inference and no re- training is necessary for different font styles. Different from many-shot method, the few-shot models are trained using the selected 405 font librarics. During inference, few-shot methods take few glyphs (i.e. 1, 2, 4, 8) references to synthesize the font library rather than training a new model. To make a fair comparison, we use the 1000 glyphs split from the test set to evaluate all models.
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4.4 Quantitative evaluation
We assess the quality of the generated images in two aspects. Firstly, Intersection Over Union (IOU) is used to measure the similarity between the synthesized and the ground truth. Higher TOU score indicates better result. As shown in Table I randomly sample four reference images from the 5763 glyphs one test font, based on which each model generates 1000 glyphs. There are a total number of 6 fonts in the test dataset. The metrics in the table are calculated based on the 1000 x 6 glyphs for each model and averaged. Table shows that our method outperforms previous state-of-the-art few-shot approaches by wide margin and generate comparable results to the many-shot approaches. Howerver, our method needs only few reference samples and one-time training. On the contrary, many-shot methods demand re-training using thousands of glyphs for each new font style.
IOU can evaluate the similarity in pixel-level, but cannot represent the style and content in essence. In addition, pixel-based metries are often inconsistent with human visual percep- tion. To improve the reliability of the evaluation, we trained two classifiers to identify the content and style classifications respectively and then use them to evaluate model prediction accuracies for the test set. As shown in Figure d. MX-Font and FUNIT show comparable performance in terms of style accuracy, but they show lower performance in content preser- vation. AGIS-Net shows comparable performance in content accuracy, but weak in style. In other words, FUNIT and MX-Font focus only on styling and fails to preserve content. structure. AGTS-Net focuses on content and fails to transfer more style.
4.5 Qualitative analysis
To demonstrate the model's performance visually, we evaluate the models using the remain- ing fonts in the test set reference glyphs. Figure g presents the many-shot and few-shot methods generated images from the test dataset. It is observed that many-shot methods except ZiGAN always fail content. preservation as shown in the red box. Among few-shot methods, FUNIT learns the style but the local structure in the generated glyphs is weak as highlighted by the yellow box. Compared with FUNIT, AGTS-Net has better performance in content preservation. However, it has poor performance in capturing global and local styles (black box). MX-Font also succeeds in learning the style of the reference, but the content structure is unsatisfactory (blue box). Overall, our proposed method generated best results qualitatively compared to other methods both in terms of content preservation and style transfer.
Conclusion
In this paper, we propose a glyph-attention module (GANet) for few-shot font generation. GANet requires only few reference glyphs as input to generate high-quality glyph images of the same style for any Chinese character. The model includes three trainable encoders to extract query, content and style features, and two glyph-attention modules query global content and local style features, and a decoder for merging queried features to obtain stylized glyph images. To achieve better performance, multi-task discriminators are employed to minimize the distribution difference between the generated results and the ground truth. Quantitatively and qualitatively, the experimental results show that our model accomplishes the stale-of-the-art compared with other cutting-edge lew-shot glyph generation methods.
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A Appendix
A.1 Network architecture
I R128X128X3 y/ E
x E R8x8x512
ResBlock down 64
ResBlock up 512
20 E RI288128X3 128X3
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Rlock 512
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ResBlook down 128
ResBlock down 512
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Global sum pooling
ResBlock up 64
Res 512
Relu
ResB 64
(a) Encoder
BN, ReLU, Conv 3, Tanh
Inner product
Dense 1
(b) Decoder
(c) Discriminator
Tables (a), (b) and (c) present network architectures of the encoder, decoder and discrim- inator respectively. The proposed GANct consists of three encoders (i.c., qucry, content and style encoders). They have the same architecture but don't share the weights. The two discriminators (content and style) also have the same architecture and don't dohare the weights, The_slight difference between them lies in the inner product module Miyato & Koyama (2018), due to the difference in the count of labels in content and,style. The de- coder's 's imput is feature map that is from,the encoders as defined in L. And all three network architectures consist of ResBlook Miyato & Koyama (2018).
A.2
Ablation study
A.2.1 Does glyph-attention really work?
In order to verify the effectiveness of content and style glypl-atteption modules, we control the feature flows before being fed into the decoder as defined in [ As shown in Figure 3 firstly, only feed the query feature to the decoder (the first row) and the synthesized glyphs fail to capture content because the query features carry little content information. Secondly, when switch the query and content feature flows at the same time (the
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second row), the synthesized glyphs bear correct content without correct style. When switch on the query and style flows, there is font style but no content information as input. The hypothesis is that the content glyph-attention module mainly focuses on the global features of the content set, so + e can synthesize correct content. On the other side, the style glyph-attention module query the local style features from the style set. As shown in Fighre E. q+ s shows chaotic results in many local style patches. Finally, when we switch on query, content and style feature flows, the synthesized results contain both correct content and style.
q+c d
q+s
q+s+c
Target
q+c
q+s 1ス
",
# BI
q+S+c
Target
Figure 5:
The function of glyph-attention. The first column q, e and s are noted as query, content and style feature flow respectively
A.2.2 Relationship between shot and content-style
Because glyph-attention modules query content and style features from the content and style sets, the length of which impact the performance of the model. In order to figure out the relationship between shot number and model performance, we use two classifiers to identify the sypthesized glyphs from the content and style aspects respectively. On the left side in Figure g. we fix the length of the style set to eight and vary the shot number the content set. It is found that when the shot number changes from to 2, the content top-1 accuracy improves significantly. However, the performance gain limited when the shot number increases from 2 to 16. Similar observation found in the right side of the figure.
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Content
Style
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Figure 6: Top-1 accuracy of content and style. On the left figure, fix the count of style set to 8, and change the shot number content set; on the right figure, fix the count of content set to and change the shot number of style set.
A.2.3 Interpolation in content and style
To show that our content and style representations age semantically meaningful, we provide the content and style interpolation results in Figure 7 In the content interpolation, we can observe that the style is maintained when interpolated from one glyph to another. Whereas in the style interpolation, the content structure is well preserved. It is concluded that our model is able to disentangle the content and style representations well.
alpha
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content Interpolation
style interpolation
Figure 7: Interpolation in content and style. For thr content interpolation, because the query also contain the content information, we use c feature that from content glyph-attention module and query encoder the interpolation content feature. For the style interpolation, we just use the feature that is from style glyph-attention module to interpolate.
A.3 More synthesised results
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Ref
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Figure 8: More synthesiscd results. Style set and content set are both contain 4 glyphs to synthesis new font.
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Ref
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Figure 9: More synthesised results. Style set and content set are both contain glyphs to
syuthesis new font.
15


Reviews:
[{"review_id": "lDF2yc5qGrb", "reviewer": "Reviewer_6xeo", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "\n[Paper strengths]  \nThe motivation is clear and reasonable. Different from image style transfer which usually adopts Ada-IN to transfer global and statistic information (e.g., texture and color), the paper proposes style and content glyph-attention modules to extract style and content features from custom glyph sets. \n\n[Paper weakness]  \nExperiment:\n1. The experiment setting and comparison need to be clarified:\n  - In Section 4.1, the paper claims that the proposed methods use 8 style reference images and 8 content reference images. In contrast, in Fig 4, the number of styles and content reference images are used for the compared methods and the proposed model should be explained.\n  - Which fonts does the model use as query images and content reference images?\n2. More evaluation metrics for image generation should be used, such as FID、LPIPS.\n3. Lacks comparison to latest font generation methods:   \n   [1] Few-shot Font Generation with Localized Style Representations and Factorization. S Park, S Chun, J Cha, B Lee, H Shim. AAAI 2021.  \n   [2] DG-Font: Deformable Generative Networks for Unsupervised Font Generation.\nY Xie, X Chen, L Sun, Y Lu. CVPR 2021.  \n   [3] RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering. Y Huang, M He, L Jin, and Y Wang. ECCV 2020.  \n\nModeling:  \n4. The query image is one of the content images. What attention does content glyph attention learn? I would like to see the visualization of the attention map for both style glyph attention and content glyph attention.  \n5. The writing is not clear. In Sec 3.3.3, more explanation and notation should be clarified. Also, I would like to know how many binary classification heads are used for content and style discriminators. \n", "summary_of_the_paper": "The paper proposes a glyph-attention network for few-shot font generation. They claim that fonts’ content features are basically global features and style features are related to local features. They propose a style glyph attention to capture the global features from the content set of glyphs, and a content glyph attention module to encapsulate pattern information from the style set of glyphs. The motivation is clear and reasonable but there are still some major issues in model construction and the experiment.", "summary_of_the_review": "Overall, the paper’s motivation is clear and reasonable. However, there are still some issues on modeling and experiments that need to be clarified. Also, the overall writing is not clear.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "cR-S6vaD9xf", "reviewer": "Reviewer_ihg3", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n+ the paper is well written and easy to follow in most parts\n+ the design of glyph attention component fits with problem well\n+ experiment results are comprehensive\n\nWeaknesses:\n- I am confused why there is a query and a separate content set. They all have the same content, and there seems to be no requirement on their styles. Then why not treat them in the same way? If we have to have a single query, how to select the query?\n- The method uses 400+ Chinese fonts as training data, and 6 as test set. It is very likely there are some training fonts very similar to the test ones. So there should be a very strong baseline where you can find the most similar training fonts to the query, and directly use the glyph in the similar training font with the same content as target. The authors should provide such baseline results to illustrate how novel the test font styles are compared with what's in the training set.\n\nMinor issues:\n- The citation format in many places is incorrect.\n- Why both the style set and glyph set needs to have the same number of glyphs?\n- The description \"k_s and v_s are equal to F_s\" in Sec 3.1.2 is somewhat confusing. I think this is basically a self attention model. The \"key\" and \"value\" are used more often in the context of a memory network.\n\n ", "summary_of_the_paper": "This paper considers Chinese glyph font style transfer problem with few reference inputs. The model has three encoders for query, style and content references; one decoder for target generation; and two discriminators for style and content. The main novelty comes with the glyph attention design with both local and global structure for style and content.", "summary_of_the_review": "I like the design of the glyph attention part of the model. However, I am concerned about the use of a query and a content set with unclear relationship between them. Also, seeing the baseline comparison proposed above will give more evidence about how challenging is the problem and how well the method performs. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "W_6-jS257Fx", "reviewer": "Reviewer_oLEK", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Detailed analyses on the strengths and weaknesses of the paper are listed as follows:\n\n-- Strengths of the paper:\nFew-shot font synthesis is a challenging and ongoing task in the areas of CV, CG, and AI. Existing approaches mainly rely on the disentanglement of glyph content and style. Typically, two encoders that aim to extract content and style features, respectively, are adopted, and content and style information are disentangled by applying various techniques and/or training strategies. The proposed GANet utilizes a different network architecture motivated by the attention mechanism. Such kind of network design is novel in solving the problem of font synthesis. The utilization of style glyph-attention and content glyph-attention seems reasonable for few-shot font generation. Both quantitative and qualitative experiments have been conducted to verify the effectiveness of the proposed method. To sum up, I think the key strength of this paper is to design a novel network inspired by the self-attention mechanism to handle the challenging task of few-shot font generation.\n\n-- Weaknesses of the paper:\n1) Some technical details of the proposed method are doubtful, where more explanations and experimental evidence are required. More specifically, the query branch plays an important role in the whole network but why the branch should be designed in the current form is unclear. Basically, since the content of the query glyph image is the same as glyph images in the content set, applying the self-attention operation seems enough to achieve the content glyph attention. Why should we use a glyph image with the same content in a different font style as the query for the content attention module? Similarly, many readers might also raise the question of why the glyph image with the target content in a different style can be used as the query for the style attention module? It seems that there is no relationship regarding the target style information between the query feature and the value (key) feature in the style glyph attention module. Furthermore, directly adding the outputs of the two attention modules with the query feature also seems less meaningful compared to the concatenation of these three features. Synthesis results of the proposed methods with these two settings should be compared. At last, the authors simply mentioned that the query glyph image should be with the same content as the content set but in a new style, will the selection of different query font styles affect the performance of GANet? \n\n2) Experimental results presented in this paper are not convincing enough to support the authors’ statement that “Less number of samples are needed and better performance is achieved when compared to the other state-of-the-art few-shot font generation methods”. Firstly, during inference, only 8 glyph images in the target style are required to be fed into the style encoder as reference and no finetuning operation is needed. Therefore, the quality of synthesized glyph images might heavily rely on the training dataset. Namely, when the target font style is similar to some fonts in the training dataset or can be properly interpolated using those existing font styles, high-quality synthesis results will be obtained. Otherwise, few-shot generative models without fine-tuning will more likely perform unsatisfactorily. Therefore, it is important to show the nearest neighbors of synthesized glyph images in the training dataset to demonstrate that the model is still capable of handling unseen and special font styles. For example, the authors should construct the test dataset by selecting some fonts with quite special styles or asking professional designers to create some glyph samples in artistic font styles which are quite different from those in the training dataset. However, as we can see from the experiment section (see Fig. 3), the 6 fonts in the test dataset all possess commonly-seen font styles. Most probably, fonts with very similar styles as those 6 fonts can be found in the training dataset. At least, the NNs of the synthesized glyph images in the training dataset should also be shown in Fig. 3. What is worse, when we zoom in Fig. 3, it can be clearly observed that there exist many artifacts/distortions in the synthesis results of GANet, especially in the last two columns, qualitatively denying the authors’ conclusion that their proposed GANet outperforms other state-of-the-art few-shot font synthesis methods. \n\n3) There exist many typos and informal expressions. The whole manuscript needs careful proofreading to improve the presentation quality. Here, I just list some examples of the writing problems and more can be found by carefully checking. \n-1- The “\\cite” commend should be used instead of “\\shotcite”, which results in wired sentence like “Lastly, Adversarial lossesGoodfellow et al. (2014)…” in the abstract: \n-2- In Section 3.2.1: “font style mainly depend on local stroke details,” ->?\n-3- Below Eq. (7): ” Where Φ3_1 is …” ->”where Φ3_1 is …”\n-4- In Section 2.3: “(SAGAN) Zhang et al. (2019) ……. (non-local) Wang et al. (2018) …” ->?\n-5- In Page 5: “font does not have texture and color, just consists of some white and black strokes.” -> ?\nand more…\n", "summary_of_the_paper": "This paper proposed a few-shot font generation method, GANet. The key idea is to design glyph-attention modules including the style glyph-attention module and the content glyph-attention module to recover the glyph in the target style from the queried glyph. The multi-task adversarial loss was also employed to further improve the synthesizing performance. Experiments conducted on a dataset constructed by the authors verified the effectiveness of the proposed method in handling the task of few-shot Chinese font synthesis.", "summary_of_the_review": "As analyzed in my above comments, I think the quality of this paper is slightly below the bar of ICLR. I will be happy to read the author's responses and discuss with other reviewers to make my final decision.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "PV5Z-3ej-ue", "reviewer": "Reviewer_GLJu", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n1.\tThe proposed content and style glyph-attention modules are novel to some degree;\n2.\tThe proposed framework outperforms other baseline methods quantitatively and qualitatively.\n\nWeaknesses:\n1.\tTechnical novelty is limited. The proposed framework is essentially a transformer variant. Although this work is probably the first to apply a Transformer-like model on few-shot font generation, there exist works that have attempted to apply in closely related tasks, like style transfer [1]. \n2.\tEvaluation is insufficient. For quantitative comparison, only IoU and classification accuracy are provided. It will be more convincing to provide comparison results on FID and SSIM to show the effectiveness of the proposed framework.\n3.\tReferences and baseline methods are missing. [2] and [3] are both proposed for font generation but are not mentioned. \n4.\tExperimental details are not clear. (1) how to conduct the classification task on content, as mentioned in Section 4.4? 2. As mentioned in Section 3.3.3, the discriminators are conditional. What is the input of the discriminators?\n5.\tMore insights are needed. It would be better to move the discussion in the Appendix to the main body of the manuscript.\n\nReferences:\n[1] StyleFormer: Real-time Arbitrary Style Transfer via Parametric Style Composition, ICCV 2021\n[2] Multi-Content GAN for Few-Shot Font Style Transfer, CVPR 2018\n[3] DG-Font: Deformable Generative Networks for Unsupervised Font Generation, CVPR 2021\n", "summary_of_the_paper": "In this submission, the authors proposed a framework for few-shot font generation where the key components are content and style glyph-attention modules. They demonstrated the effectiveness of the proposed framework on a newly-collected dataset by comparing the IoU of different methods.", "summary_of_the_review": "This paper proposed a Transformer-like model for few-shot font generation. However, its technical novelty is limited, evaluation is not sufficient, and the discussion is not thorough. The authors can improve this paper by including more recent works as baselines and comparing them using more evaluation metrics.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a framework for few-shot font generation. Reviewers thought that the model was well-suited to the task. They were split on clarity, with some saying that the paper was easy to follow and others saying that it lacked sufficient detail. Overall reviewers found the technical novelty limited, saying that the approach was a “transformer variant”, while it was the first to apply a Transformer-like model on few-shot font generation, there wasn’t sufficient novelty in this task to have broad appeal to the ICLR community. The reviewers also pointed out some deficiency in the evaluation, concerning the chosen metrics (multiple reviewers requesting fidelity metrics) and missing baselines. Some reviewers posed questions to the authors but the authors did not respond to the reviews. There is a clear consensus to reject the paper.
Decision: Reject