Abstract: Systems and methods are provided for determining a first upper bound of a x-direction frame margin of a downsampled image and a second upper bound for a y-direction frame margin of the downsampled image. A total value for each row and column of a weighted grayscale image formed from the downsampled image may be determined. An outlier filtering may be applied to the determined total values for each row and column of the weighted grayscale image. A peak of pairwise ratios of consecutive values from left and right sides of the weighted grayscale image may be used to determine an x-margin, and a peak of pairwise ratios of consecutive values from top and bottom sides of the weighted grayscale image may be used to determine a y-margin. Content may be applied to the image based on the determined x-margin and y-margin values to form a composite image.

Abstract: A method receives an input file. The input file includes a plurality of entries where different entries are associated with a different characteristic. A selection of a guide for an entry is determined where a guide includes guidelines for formatting content in an entry in the plurality of entries. The method performs analyzing an image from the entry to generate image variations. Then, the method analyzes the image and the image variations to place text items from the entry in the image and the image variations where the text items in the image and the image variations are formatted based on the guidelines in the guide for the entry. A plurality of design variants is generated for the entry based on the image and the image variations that include text items.

Abstract: Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.

Abstract: Systems and methods are provided for receiving at least a first query color, and searching an electronic catalog including a plurality of product images for the first query color to determine a similarity measure between the first query color and a product image of a plurality of product images. The similarity measure may be determined by determining a Euclidean distance between values in a three-dimensional color space for the first query color and a target color of the product image, and determining the similarity measure between the query color and the product image by determining a sum of the similarity measures from all target colors on the product image, weighted by the coverage of each target color. The search results may be transmitted based on the searching of the electronic catalog including the plurality of product images for the first query color.

Abstract: Systems and methods are provided for receiving an image that includes a clothed person, determining a pose of the person in the image, and segmenting the image into one or more first fashion items. One or more second fashion items may be determined using a similarity search that searches at least one storage device communicatively coupled to the server based on the one or more first fashion items. At least one outfit proposal may be generated based on the one or more second fashion items. Image re-stylization of corresponding portions of the image may be performed, including the clothed person to generate recommended outfit images based on the at least one outfit proposal. The generated outfit images may be transmitted for display.

Abstract: A method for data processing is described. The method includes receiving, from a client of a web service, a request for a web component included in a web page supported by the web service, where the web component is associated with a data structure that defines design characteristics of the web component. The method further includes determining configuration parameters that define stylistic characteristics of the web component based on data attributes of the web page and the data structure associated with the web component, where the configuration parameters are determined using a model trained on a corpus of web pages supported by the web service. The method further includes transmitting, to the client and in response to the request, an indication of the configuration parameters for the web component, where the indication is configured to display the web component according to the stylistic characteristics defined by the configuration parameters.

Abstract: Systems, devices, and techniques are disclosed for conversation topic extraction. Text of a communication channel may be received. The text of the communication channel may be divided into conversation documents based on conversation threads of the communication channel. Phrases of the text of the conversation documents may be tokenizes. Topic phrases for the conversation documents may be determined by assigning importance scores to the tokenized phrases using unsupervised topic extraction. The topic phrases may be the tokenized phrases with the highest importance scores.

Abstract: Systems, devices, and techniques are disclosed for visual text summary generation. An input text may be received. Keywords may be extracted from the input text. Representative keywords may be generated from the keywords. A graph representation of the representative keywords may be generated. Images associated with the representative keywords may be received. A visual-representation style may be selected based on the graph representation of the representative keywords. The mages associated with the representative keywords may be arranged according to the selected visual-representation style and the graph representation of the representative keywords.

Abstract: Systems and methods are provided for receiving at least a first query color, and searching an electronic catalog including a plurality of product images for the first query color to determine a similarity measure between the first query color and a product image of a plurality of product images. The similarity measure may be determined by determining a Euclidean distance between values in a three-dimensional color space for the first query color and a target color of the product image, and determining the similarity measure between the query color and the product image by determining a sum of the similarity measures from all target colors on the product image, weighted by the coverage of each target color. The search results may be transmitted based on the searching of the electronic catalog including the plurality of product images for the first query color.

Abstract: Systems, devices, and techniques are disclosed for style-based dynamic content generation. A seed image, entity design data, entity style data, and text items may be received. Bounding boxes that identify areas of the seed image for the placement of the text items may be generated for the seed image. Variant images may be generated from the seed image, the entity design data, and the entity style data. The variant images may be generated by placing text items in the bounding boxes based on the entity design data and rendering text of the text items using the entity style data.

Abstract: Techniques are disclosed for automatically converting a layout image to a text-based representation. In the disclosed techniques, a server computer system receives a layout image that includes a plurality of portions representing a plurality of user interface (UI) elements included in a UI design. The server computer system transforms, via executed of a trained residual neural network (ResNet), the layout image to a text-based representation of the layout image that specifies coordinates of bounding regions of the plurality of UI elements included in the UI design, where the text-based representation is usable to generate program code executable to render the UI design. The disclosed techniques may advantageously automate one or more portions of a UI design process and, as a result save time and computing resources via the execution of an image to text-based conversion ResNet machine learning model.

Abstract: Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.

Abstract: Systems, devices, and techniques are disclosed for automatic product description generation. A first set of features including labels including words may be generated from an image using a first feature extraction model. A second set of features including labels including words may be generated from the image using a second feature extraction model. A text description of a product depicted in the image may be generated by inputting the image and metadata for the image to a description generating model. The text description may include words. Each of the words may be generated by assigning probabilities to candidate words, boosting the assigned probabilities of candidate words that are similar to words of labels of the first set of features or words of labels of the second set of features, and selecting one of the candidate words based on the assigned probabilities after the boosting as a word of the text description.

Abstract: A server of a generative adversarial network (GAN) for color selection; generates a training set of color palettes. A color palette generator of the server generates a first set of color palettes based on the training set of color palettes. The first set of color palettes may be compared with a reference set of color palettes to predict a curated set of color palettes. Colors from the curated set of color palettes may be removed that are within a predetermined distance from one another in a color space. The GAN may be validated by performing cluster analysis to determine outlier latent dimensions to be changed for the color selection by the GAN. Proposed color palettes may be generated based on the GAN to be displayed on a display device.

Abstract: Systems, device and techniques are disclosed for training data generation for visual search model training. A catalog including catalog entries which may include images of an item and data about the item may be received. Labels may be applied to the images of the items based on the data about the items. The images of the items may be sorted into clusters using cluster analysis on the labels. Each cluster may include labels as categories of the cluster. Additional images may be received based on searching for the categories. Generative adversarial network (GAN) training data sets may be generated from the images of the items, the additional images, and the categories. GANs may be trained with the GAN training data sets. The GANs may generate images including images of generated items, which may be replaced with images of items from the catalog entries to create feature model training images.

Abstract: Systems, device and techniques are disclosed for multi-label product categorization. A catalog entry and a list of categories may be received. The catalog entry may be associated with an item. A textual description may be generated by comparing words in the catalog entry to existing vocabularies of words and applying part-of-speech tagging to the catalog entry. A feature vector may be generated from the textual description by applying any of token frequency feature creation, term frequency-inverse document frequency feature creation, and pre-trained word embeddings to the textual description. A set of probabilities may be determined by inputting the feature vector into a machine learning model. The set of probabilities may include a probability for each category in the list of categories.

Abstract: Systems and method are provided for generating, at a server of a generative adversarial network (GAN) for color selection, a training set of color palettes. A color palette generator of the server generates a first set of color palettes based on the training set of color palettes. The first set of color palettes may be compared with a reference set of color palettes to predict a curated set of color palettes. Colors from the curated set of color palettes may be removed that are within a predetermined distance from one another in a color space. The GAN may be validated by performing cluster analysis to determine outlier latent dimensions to be changed for the color selection by the GAN. Proposed color palettes may be generated based on the GAN to be displayed on a display device.

Abstract: Techniques are disclosed relating to automatically synthesizing user interface (UI) component instances. In disclosed techniques a computer system receives a set of existing UI elements and a set of design rules for the set of existing elements, where design rules in the set of design rules indicate one or more allowed states for respective UI elements in the set of existing UI elements. The one or more allowed states may correspond to one or more visual characteristics. Using the set of existing UI elements, the computer system may then automatically generate a plurality of UI component instances based on the set of design rules, where a respective UI component instance includes a first UI element in a first allowed state. The computer system may then train, using the plurality of UI component instances, a machine learning model operable to automatically generate UI designs.

Abstract: Techniques are disclosed relating to automatically synthesizing user interface (UI) component instances. In disclosed techniques a computer system receives a set of existing UI elements and a set of design rules for the set of existing elements, where design rules in the set of design rules indicate one or more allowed states for respective UI elements in the set of existing UI elements. The one or more allowed states may correspond to one or more visual characteristics. Using the set of existing UI elements, the computer system may then automatically generate a plurality of UI component instances based on the set of design rules, where a respective UI component instance includes a first UI element in a first allowed state. The computer system may then train, using the plurality of UI component instances, a machine learning model operable to automatically generate UI designs.

Abstract: Generating, at a server of a generative adversarial network (GAN) for color selection, a training set of color palettes. A color palette generator of the server generates a first set of color palettes based on the training set of color palettes. The first set of color palettes may be compared with a reference set of color palettes to predict a curated set of color palettes. Colors from the curated set of color palettes may be removed that are within a predetermined distance from one another in a color space. The GAN may be validated by performing cluster analysis to determine outlier latent dimensions to be changed for the color selection by the GAN. Proposed color palettes may be generated based on the GAN to be displayed on a display device.