We address the task of theme-based word typography: given a word (e.g., exam) and a theme (e.g., education), the task is to automagically produce a doodle for the word in that theme as seen in Figure 1. Concretely, the task is to replace each letter in the input word with a clipart from the input theme to produce a doodle, such that the word and theme can be easily identified from the doodle. Solving this task would be of value to a variety of creative applications such as stylizing text in advertising, designing logos – essentially any application where a message needs to be conveyed to an audience in an effective and concise manner.
Using graphic elements to emphasize the meaning of a word in reference to a related theme is referred to in graphic design as semantic reinforcement. This can be achieved in a number of ways, e.g., using different fonts and colors (Figure 1(a)), changing the position of letters relative to one another (Figure 1(b)), arranging letters in a specific direction or shape (Figure 1(c)), excluding some letters (Figure 1(d)), adding icons near or around the letters (Figure 1(e)), or replacing letters with icons (Figure 1(f)). In our work, we focus on this last type, i.e., semantic reinforcement via replacement.
This is a challenging task even for humans. It not only requires domain-specific knowledge for identifying a set of relevant cliparts to choose from, but also requires creative abilities to be able to visualize a letter in a clipart, and choose the best clipart for representing it.
The latter alone is challenging to automate – both from a training and evaluation perspective. Training a model to automatically match letters to graphics is challenging because there is a lack of large-scale text-graphic paired datasets in each domain that might be of interest (e.g., clipart, logogram). Evaluation and thus iterative development of such models is also challenging because of subjectivity and inter-human disagreement on which letter resembles which graphic.
We present a computational approach – Thematic Reinforcement of Artistic Typography (TReAT) – to generate doodles (TReATs) for semantic reinforcement of text via replacement. We represent letters in different fonts and cliparts from the Noun Project. in a common latent space. These latent representations are learned such that they have two characteristics: (1) The letters can be correctly recognized (e.g., a vs. b) in the latent space and (2) The letters and cliparts can be reconstructed accurately from the latent space. A reconstruction loss ensures that letters and clipart that are close in the latent space also look similar in the image space. A classification loss ensures that the latent space is informed by discriminative features that make one letter different from the other. This allows us to match cliparts to letters in a way that preserves distinctive visual features of the letters, making it easier for humans to identify the letter being depicted by a clipart. At test time, given a word and a theme as input, we first retrieve cliparts from the Noun Project that match that theme. For each letter in the word, we find the theme-relevant clipart which minimizes the distance from it across a variety of fonts. If the distance is low enough, we replace the letter with the clipart.
We run human studies to show that subjects can reliably recognize the word as well as the theme from our TReATs, and find them creative relative to meaningful baselines.
Our contributions are as follows:
We present TReAT – an unsupervised approach to creatively stylize a word using theme-based cliparts.555Our code will be released on GitHub.
We define a set of evaluation metrics for this task and evaluate our approach under these metrics.
We show that our approach outperforms meaningful baselines in terms of word recognition, theme recognition, as well as creativity.
Early human communication was through symbols and hieroglyphs [Frutiger1989], [Schmandt-Besserat2015]. This involved the use of characters to represent an entire word, phrase or concept. Then language evolved and we started using the alphabet for creation of new words to represent concepts. However many languages (e.g. Chinese and Japanese) still make use of pictograms or logograms to depict specific words. Today, symbols and logos are used for creative applications to increase the communication bandwidth – to convey abstract concepts, express rich emotions, or reinforce messages [Shiojiri and Nakatani2013], [Clawson et al.2012], [Takasaki and Mori2007]. Our work produces a visual depiction of text by reasoning about similarity between the visual appearance of a letter and clipart imagery. We describe prior work in each of these domains: creativity through imagery and creativity through visual appearance of text.
Creativity through imagery
There is previous work on evoking emotional responses through the modification of images. Work on visual blending of emojis combines different concepts to create novel emojis [Martins, Cunha, and Machado2018]. Visual blending has also been explored for combining two animals to create images depicting fictional hybrid animals [Martins et al.2015]. Our approach tries to induce creativity by entirely replacing a letter with a clipart. Towards the end of the paper we briefly describe a generative modeling approach we experiment with. That can be thought of as blending between a letter and a clipart. However, the motivation behind our blends is different in that we intend to generate an output that looks like both the clipart and letter, as opposed to blending specific local elements of both images to create a new concept.
Work on Vismantic [Xiao and Linkola2015] represents abstract concepts visually by combining images using juxtaposition, fusion, and replacement. Our work also represents a theme via replacement (replacing letters with cliparts); however our replacement is for the purposes of lexical resolution, not visual. Recently, there has been an exploration of neural style transfer for logo generation [Atarsaikhan, Iwana, and Uchida2018]. This work however only transfers color and texture from the style to the content. Unlike our approach, it does not alter the shape. GANvas Studio 666https://ganvas.studio/ is a creative venture that uses Generative Adverserial Networks (GANs) [Goodfellow et al.2014] to create abstract paintings. GANs have also been used for logo generation [Sage et al.2018].
Recently Google’s QuickDraw! and AutoDraw based on sketch-rnn [Ha and Eck2018]
have gained a lot popularity. Their work trains a recurrent neural network (RNN) to construct stroke-based drawings of common objects, and is also able to recognize objects from human strokes. One could envision creating a doodle by writing out one letter at a time, that AutoDraw would match to the closest object in its library. However, these matches would not be theme based. Iconary777https://iconary.allenai.org/ is a very recent pictionary-like game that users can play with an AI. Relevant to this work, user drawings in Iconary are mapped to icons from The Noun Project to create a scene.
Creativity through visual appearance of text
Advances in conditional GANs [Mirza and Osindero2014] have motivated style transfer for fonts [Azadi et al.2018] through few-shot learning. Work on learning a manifold of fonts [Campbell and Kautz2014]
allows everyday users to create and edit fonts by smoothly interpolating between existing fonts. The former explores the creation of unseen letters of a known font, and the latter explores the creation of entirely new fonts – neither add any theme-related semantics or additional graphic elements to the text.
Work on neural font style transfer between fonts [Atarsaikhan et al.2017] explores the effects of using different weighted factors, character placements and orientations in style transfer. This work also has an experiment using icons as style images, however the style transfer is only within the context of visual features of icons such as the texture and thickness of strokes, as opposed to direct replacement.
An interesting direction for expressing emotions through text has been explored through papers on colorful text [Mohammad2011], [Kawakami et al.2016]. These works attempt to learn a word-color association. Their results show that even though abstract concepts or sequences of characters may not be physically visualizable, they tend to have color associations which can be realized.
In this section, we first describe our procedure for collecting training data, and then our model and its training details. Finally, we describe our test-time procedure to generate a TReAT, that is, obtaining theme-based clipart matches for an input word and theme. A sketch of our model along with examples for training and testing are shown in Figure 3.
For our task we need two types of data for training – letters in different fonts, and cliparts. Note that we do not need a correspondence between the letters and cliparts. In that sense, as stated earlier, our approach is an unsupervised one.
For clipart data, we use the Noun Project – a website that aggregates and categorizes symbols that are created and uploaded by graphic designers around the world. The Noun Project cliparts are all 200 200 in PNG format. The Noun Project has binary cliparts, and will result in TReATs of the style shown in Figure 1. Different choices of the source of cliparts can result in different styles, including colored TReATs as shown in Figure 1(f). We downloaded a random set of 50k cliparts from the Noun Project.
We obtain our letter data from a collection of 1400 distinct font files. 999These font files (TTF) were obtained from a designer colleague. On manual inspection, we found that this set contained a lot of visual redundancies (e.g. the same font being repeated in regular and bold weight types). We removed such repetitions. We also manually inspected the data to ensure that the individual letters were recognizable in isolation, and discarded overly complicated and intricate font styles. This left us with a total of 777 distinct fonts. We generated 200 200 image files (PNG format) from each font file for the entire alphabet (uppercase and lowercase) giving us a total of 40.4k images of letters (777 fonts 26 letters in the English alphabet 2 (upper and lower cases)).
Our primary objective is to find visual similarities between cliparts and letters in an unsupervised manner. To this end, we train an autoencoder[Ballard1987] with a reconstruction loss on both clipart and letter images (denoted by ). We denote a single input image by . Each input image is passed through an encoder neural network and projected to a low dimensional intermediate representation . Finally, a decoder neural network tries to reconstruct the input image as , using the objective ,
where is the sum over squared pixel differences between the original image and its reconstruction. We set the dimensionality of to be
. In addition to the reconstruction objective, we utilize letter labels (52 labels for lowercase and uppercase letters) to classify the intermediate representationsfor the letter images. This objective helps the encoder discriminate between different letters (possibly with similar visual features) while clustering together the intermediate representations for the same letter across different fonts. This would allow the intermediate representation to capture visual features that are characteristic of each letter, and when cliparts are matched to letters using this representation, the matched cliparts will retain the visually discriminative features of letters.
Concretely, we project
to a 52-dimensional space using a single linear layer with a softmax non-linearity and use the cross entropy loss function. Letand
be the parameters of a linear transformation of
. We obtain a probability distributionacross all labels as,
Let be the subset of images in that are letters. We maximize the probability of the correct label corresponding to each letter image .
Note that the same is used in both objective functions for letter images. These objectives are jointly trained using a multitask objective
Our final loss function is thus composed of two different loss functions: (1) trained on both letters and cliparts, and (2) trained only on letters. Here
is a tunable hyperparameter in the range. We set to 0.25 after manually inspecting outputs of a few word-theme pairs we used while developing our model (different from the word-theme pairs we use to evaluate our approach later).
Our encoder network is an AlexNet [Krizhevsky, Sutskever, and Hinton2012]convolutional neural network trained from scratch, made up of 5 convolutional and 3 fully connected layers. Our decoder network consists of 5 deconvolutional layers, 3 fully connected layers and 3 upsampling layers. We use batch norm between layers.101010Implementations of our encoder and decoder were adapted from https://github.com/arnaghosh/Auto-Encoder.
We use ReLU activations for both the encoder and decoder. We use the Adam optimizer with a learning rate of, and a weight decay of . The input dataset is divided into minibatches of size 100 with a mixture of clipart and letter images in each minibatch. We use early stopping based on a validation set as our stopping criterion.
We resize our images to 224 224 using bilinear interpolation to match the input size of our AlexNet-based encoder. We normalize every channel of our input data to fall in .
At test time given a word and a theme, we retrieve a theme-relevant pool of cliparts (denoted by ) by querying Noun Project. If multiple phrases have been used to describe a theme, we use each phrase separately as a query. We limit this retrieval to no more than 10,000 cliparts for each phrase. We then combine cliparts for different phrases of a theme together to form the final pool of cliparts for that theme. For example, for the theme countries, continents, natural wonders, we query Noun Project for countries, continents and natural wonders individually and combine all retrieved cliparts together to form the final pool of theme-relevant cliparts. On average across 95 themes we experimented with, we had a minimum of 49 and maximum of 29,580 cliparts per theme, with a mean of 9731.2 and median of 9966. We augmented this set of cliparts with left-right (mirror) flips of the cliparts. This improves the overall match quality. E.g., in cases where there existed a good match for J, but not for L, the clipart match for J, when flipped, served as a good match for L. Similarly for S and Z.
For each letter of the input word, we choose the corresponding letter images (denoted by ) taken from a predefined pool of fonts. To create the pool of letter images , we used uppercase letters from 14 distinct, readable fonts from among the 777 fonts used during training. These were kept fixed for all experiments. We found that uppercase letters had better matches with the cliparts (lower cosine distance between corresponding latent representations on average, and visually better matches). Moreover, we found that in several fonts, letters were the same for both cases.
We replace the letter with a clipart (chosen from ) whose mean cosine distance in the intermediate latent space is the least, when computed against every letter image in . Concretely, if denotes the intermediate representation for the image in and is the intermediate representation for a clipart in , the chosen clipart is
We find a clipart that is most similar to the letter on average across fonts to ensure a more robust match than considering a single most similar font. In this way, each letter in the input word is replaced by its closest clipart to generate a TReAT.
We show example TReATs generated by our approach in Figure 4. We find that the word can often be difficult to recognize from the TReAT if the Noun Project cliparts corresponding to a theme are not sufficiently similar to the letters in the word. To improve the legibility of our TReATs, we first normalize the cosine distance values of our matched cliparts for the alphabet for a specific theme in the range [0, 1]. We only replace a letter with its clipart match if the normalized cosine distance between the embedding of the letter and clipart is 0.75. It is known that the first and last letters of a word play a crucial role in whether humans can recognize the word at a glance. So we use a stricter threshold, and replace the first and last letters of a word with a clipart only if the normalized cosine distance between the two is 0.45. Example TReATs with all letters replaced and only a subset of letters replaced can be seen in Figure 5. Clearly, the TReATs with a subset of letters replaced (theme-some) are more legible than replacing all letters (theme-all), while still depicting the desired theme. We quantitatively evaluate this in the next section.
We evaluate our entire system along three dimensions:
How well is our model able to learn a representation that captures visual features of the letters?
How does our chosen source of cliparts (Noun Project) affect the quality of matches?
How good are our generated TReATs?
We use t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize our learnt latent representations of letters and cliparts. Among letters, we find that our model clusters letters of different fonts together, while distinguishing between visually dissimilar letters. E.g., Figure 7 visualizes in 2D uppercase O, Q, E and F in the 14 fonts used at test time. As expected, O and Q clusters are close, and E and F clusters are close, but both these sets of clusters are apart. Visualizing letters as well as cliparts, Figure 8 shows that our model is able to learn a representation such that visually similar letter-clipart pairs are close in the latent space.
Effect of source of cliparts
Themes which have fewer cliparts, and hence lower diversity and coverage across the letters (e.g. mythical beast in Figure 8(a)) have poorer matches as compared to larger, more diverse themes (e.g. library in Figure 8(b)). Indeed, we see that recognizing the word in a TReAT generated from the former theme is significantly harder than for the latter.
Quality of TReATs
We now evaluate the quality of TReATs generated by our approach. We developed our approach on a few themes (e.g., education, Harry Potter, Halloween, Olympics) and associated words (e.g., exam, always, witch, play). To evaluate our approach in the open world, we collected 104 word-theme pairs from subjects on Amazon Mechanical Turk (AMT). We told subjects that given a word and an associated theme, we have a bot that can draw a doodle. We showed subjects a few example TReATs. We asked subjects to give us a word and an associated theme (to be described in 1-5 comma separated phrases) that they would like to see a doodle for. Example (word & theme) pairs from our dataset are (environment & pollution, dirt, wastage), (border & USA), (computer & technology). We allowed subjects to use multiple phrases to describe the theme to allow for a more diverse set of cliparts to search from when generating the TReAT. We evaluate our TReATs along three dimensions:
Can subjects recognize the word in the TReAT?
Can subjects recognize the theme in the TReAT?
Do subjects find the TReAT creative?
We compare our approach theme-some to a version where we replace all letters in the word with cliparts (theme-all) to evaluate how replacing a subset of letters affects word recognition (expected to increase) and theme recognition (expected to remain unchanged or even increase because recognizing the word can aid in recognizing the theme), as well as creativity (expected to remain unchanged or even increase because the associated word is more legible as opposed to gibberish). We also compare our approach to an approach that replaces letters with cliparts, but is not constrained by the theme of interest (notheme-some and notheme-all). We find the clipart that is closest across all 9511111195 because some themes repeat in our 104 (word & theme) pairs. themes in our dataset to replace the letter. This can result in increased word recognition because letters can find a clipart that is more similar (from a larger pool not constrained by the theme), but will result in lower theme recognition accuracy. Note that theme recognition will still likely be higher than chance because the word itself gives cues about the theme. For no-themed clipart, we compare an approach that replaces all letters (i.e., notheme-all) as well as only a subset of letters (notheme-some). Finally, as a point of reference, we evaluate a TReAT that simply displays the word in a slightly atypical font (font). We expect word recognition to be nearly perfect, but theme recognition as well as creativity to be poor. These five different types of TReATs are shown in Figure 6. This gives us a total of 520 TReATs to evaluate (5 types 104 word-theme input pairs). No AMT workers were repeated across any of these tasks.
We showed each TReAT to 5 subjects on AMT. They were asked to type out the word they see in the TReAT in free-form text. Notice the open-ended nature of the task. Performance of crowd-workers for word recognition of different types of TReATs is shown in Figure 9(a). This checks for exact string matching (case-insensitive) between the word entered by subjects and the true word. As a less stringent evaluation, we also compute individual letter recognition accuracy. These were computed only for cases where the length of the word entered by the subject matched the true length of the TReAT because if the lengths do not match, the worker likely made a mistake or was distracted. Letter recognition accuracies are shown in Figure 9(b).
As expected, leaving a subset of the letters unchanged leads to a higher recognition rate for theme-some and notheme-some compared to their counterparts, theme-all and notheme-all respectively. Also, notheme-all and notheme-some have higher word recognition accuracy than theme-all and theme-some because the clipart matches are obtained from a larger pool (across all themes rather than from a specific theme). The added signal from the theme of the cliparts in theme-all and theme-some does not help word recognition enough to counter this. notheme-all already has a high recognition rate, leaving little scope for improvement for notheme-some. Finally, font has near perfect word recognition accuracy because it contains the word clearly written out. It is not a 100% because of typos on the part of the subjects. In some cases we found that subjects did not read the instructions and wrote out the theme instead of the word itself across all TReATs. These subjects were excluded from our analysis.
We showed each TReAT to 6 subjects on AMT. The same theme can be described in many different ways. So unlike word recognition, this task could not be open-ended. For each TReAT, we gave subjects 6 themes as options from which the correct theme is to be identified. These 6 options included the true theme from the 95 themes in our dataset, 2 similar themes, and 3 random themes. The similar themes are the 2 nearest neighbor themes to the true theme in word2vec [Mikolov et al.2013] space. word2vec
is a popular technique to generate vector representations of a word or “word embeddings” which capture the meaning of the word such that words that share common contexts in language (that is, likely have similar meaning) are located in close proximity to one another in the space. If a theme is described by multiple words, we represent the theme using the averageword2vec
embedding of each word. This is a strategy that is commonly employed in natural language processing to reason about similarities between phrases or even entire sentences[Wieting et al.2016], [Adi et al.2017]. We find that 64% of the TReATs were assigned to the correct theme for theme-all, 67% for theme-some, 43% for notheme-all, 51% for notheme-some and 60% for font respectively. As expected, notheme-all and notheme-some have lower theme recognition accuracy than theme-all and theme-some because notheme-all and notheme-some do not use cliparts from specific themes. Notice that theme recognition accuracy is still quite high, because the word itself often gives away cues about the theme (as seen by the theme recognition accuracy of font that lists the word without any clipart).
This theme recognition rate is a pessimistic estimate because theme options presented to subjects included nearest neighbors to the true theme as distractors. These themes are often synonymous to the true theme. As a less stringent evaluation, we sort the 6 options for each TReAT based on the number of votes the option got across subjects.Figure 9(c) shows the Mean Reciprocal Rank of the true option in this list (higher is better). We also show Recall@K in Figure 9(d) that compute how often the true option is in the top-K in this sorted list. Similar trends as described above hold.
Comparing theme-some to theme-all, we see that replacing only a subset of letters does not hurt theme recognition (in fact, it improves slightly), but improves word recognition significantly. So overall, theme-some produces the best TReATs. We see this being played out when TReATs are evaluated for their overall creativity (next). This relates to Schmidhuber’s theory of creativity [Schmidhuber2010]. He argues that data is creative if it exhibits both a learnable or recognizable pattern (and is hence compressible), and novelty. theme-some achieves this balance.
Recall that our goal here is to create TReATs to depict words with visual elements such that the TReAT leaves an impression on people’s minds.
We now attempt to evaluate this. Do subjects find the TReAT intriguing / surprising / fun (i.e., creative)?
We showed each TReAT to 5 subjects on AMT.
They were told: “This is a doodle of [word] in a [theme] theme.
On a scale of 1-5, how much do you agree with this statement?
This doodle is creative (i.e, surprising and/or intriguing and/or fun).
1. Strongly agree (with a grin-like smiley face emoji in green)
2. Somewhat agree (with a smiley face in lime green)
3. Neutral (with a neutral face in yellow)
4. Somewhat disagree (with a slightly frowning face in orange)
5. Strongly disagree (with a frowning face in red).”
Crowd-worker ratings are shown in Figure 9(e).
theme-some was rated the highest.
We believe this is due to a good trade off between legibility and having a theme-relevant depiction that allows for semantic reinforcement.
notheme-all and notheme-some are significantly worse.
Recall that they are visual, but not in a theme-specific way.
So they are visually interesting, but do not allow for semantic reinforcement.
The resultant reduction in creativity is evident.
Interestingly, notheme-some scores slightly higher than notheme-all.
This may be because notheme-some is not more legible than notheme-all (notheme-all is already sufficiently legible).
With more of the letters visually depicted, notheme-all is more interesting. Finally, font has a significantly lower creativity score.
It is rated lower than neutral, close to the “Somewhat disagree” rating.
To get a qualitative sense, we asked subjects to comment on what they think of the TReATs.
Some example comments:
theme-all: “cool characters and each one fits the theme of the ocean”, “Its [sic] creative and represents the theme well, but I don’t see disney all that much.”
theme-some: “I like how it uses the image of the US and then a state to spell out the word and looks like something you’d remember.”, “Very fun and intriguing. I like how all the letters are pictures representing a computer mouse.”.
notheme-all: “It is creative but it has nothing to do with fear.”, “It does a very good job of spelling out CHRISTMAS, but the individual letters are not related to the holiday at all.”
notheme-some: “It is somewhat creative, especially the unicorn head for “G”, though I don’t know what any of it has to do with the theme.”, “There are too many icons that seemingly have nothing to do with the theme.”
font: “It just spells out the word, not really a doodle”, “Its not a doodle, its just the word Parrot, so I don’t think its creative at all.”
Recall that our model has an autoencoder as a component. The decoder takes as input the latent embedding and generates an image corresponding to it. This presents us with an opportunity to explore morphing mechanisms to make the TReATs more interesting. We can start with the embedding corresponding to a letter and smoothly interpolate to the embedding corresponding to the matched clipart. At each step along the way, we can generate an image that depicts the intermediate visual. This allows for increased legibility (because the original letter is visible early in the morph), as well as potential for more semantic reinforcement and intrigue as the TReAT is slowly “revealed” over time. An example of a morphed output is shown in Figure 12. The matched clipart doesn’t naturally resemble the letter X as much as it does the letter N. Morphing allows for the clipart to be transformed in a way that makes the letter (X) more apparent, while still retaining its visual identity (Harry Potter’s scar).
In this section, we discuss some drawbacks of our current model and potential future work.
No Clipart Relevance Score:
A comment from a subject evaluating the creativity of Figure 10(a) (word church & theme pastor, Jesus, people, steeple) was “you need a cross […] before the general public would […] get this.”
Our approach does not include world knowledge that indicates which symbols are canonical for themes (Noun Project does not provide a relevance score). As a result, our model can not explicitly trade off visual similarity (VS) for theme relevance (TR) – either to compromise on VS to improve TR, or to at least optimize for TR if VS is poor.
No Contextual Querying:
Multiple phrases used to describe a theme often lose context when they are used individually to query Noun Project. For example, the last clipart in Figure 10(b) for (word money & theme finance, banking, support) is of a cheerleader, and hence relevant to the phrase support, but is not relevant in the context of the finance theme.
The lack of context also hurts polysemous theme words. bat when used as a keyword with the another keyword bird refers to the creature bat, but in the context of baseball refers to sports equipment.
Imperfect Match Scores:
Our automatic similarity score frequently disagrees with our (human) perceptual notion of similarity. E.g., in Figure 11 right, the cliparts used to replace C and N in theme-all look sufficiently similar to the corresponding letters. But the automatic similarity score was low, and so theme-some chose to not replace the letters. Approaches to improve the automatic score can be explored in future work. For instance, in addition to mirror images, using rotated and scaled versions of the cliparts to augment the dataset would help.
To mitigate these concerns, we plan to build an interactive tool. Users can choose from the top- clipart matches for each letter. Users can iterate on the input theme descriptions until they are satisfied with the TReAT. Users can also leave the theme unspecified in which case we can use the word itself as the theme. Finally, users can choose which letters to replace in theme-some like TReATs.
In this work, we introduce a computational approach for semantic reinforcement called TReAT – Thematic Reinforcement for Artistic Typography. Given an input word and a theme, our model generates a “doodle” (TReAT) for that word using cliparts associated with that theme. We evaluate our TReATs for word recognition (can a subject recognize the word being depicted?), theme recognition (can a subject recognize what theme is being illustrated in the TReAT?), and creativity (overall, do subjects find the TReATs surprising / intriguing / fun?). We find that subjects can recognize the word in our TReATs 74% of the time, can recognize the theme 67% of the time, and on average “Somewhat agree” that our TReATs are creative.
We thank Abhishek Das, Harsh Agrawal, Prithvijit Chattopadhyay and Karan Desai for their valuable feedback.
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