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The plates of the world

reinterpreting a collection of mass-produced memorabilia

This experiment aims to expand the understandings of old and new collections, draws attention to the interdependence of collecting and sheds light on how collections form knowledge of cultural heritage on a larger scale.

Using a digital platform, machine learning and web scraper technologies, we aim to highlight alternative forms of collections and speculate on the future of how they get displayed. For this experiment, we ask to meditate upon this type of collection, through one specific collectible item — souvenir plates of 1939 and 1964 New York World Fair.

Step 1. locating the plates of the world

In collaboration with Queens Museum, we developed an online and accessible platform showcasing a dynamic image of mass production collectibles and the people that engage with them, open for others to add their own. In short, we set out to create a democratic collection.

I. We apply machine learning technology to expand the museum collection to include items outside of the building, looking outwards and analyzing the collection through global communities. As a start we developed an online scraping tool to automatically locate the relevant images based on keyword variation (“world’s fair plate”, “WF NYC plates”, and etc) from e-commerce website to compare with the type of plate in the original index of the museum.

II. In order to overcome the small number of sources (in this case 26) we could used to train our system, so we could improve the result and create much more accurate map of the items – we created 3D representation of each plate and rendered a synthetic dataset.

We used Photogrammetry, a 3D scanning method, to compute a 3D reconstruction. Then inserted them into Unity3D and rendered each one of them in different lighting and backdrops settings. These choices were inspired by the images people tend to upload to Etsy and Ebay to represent the items they are selling. We generated Thousands of images- and now could train the machine learning system. Another outcome of this process is 3D documentation of the collection – available to the museum for future uses.

the capture process


generating Synthetic Data


Synthetic data


III. We developed a website to display the results: With two modes of explorations: a grid view and a map view.

The grid view presents the museum’s collection, with the plates that matched it online via the scraper system.


Step 2. Generating new plates

In step 1 there were also moments in which we learn about what we consider to be a “real” part of the collection and what we perceive to be a public form of culture, and the benefit that lies in merging the two. We wished to explore this space more and push the machine learning technology further in order to offer new kind of interpretation to the existing collection.

We used a image classification to explore what the machine “sees” as a natural observation that undermine how humans collect memories. Then we generated sentences out of the extracted keywords.

We took the results and applied it to text to image algorithms, to generates a new plates based on machine creativity and knowledge.

This project explored the gap between the virtual and physical aspects of memorabilia, as well as the meaning of collecting for individuals and the authority of museums to define cultural value — in a historical and in a material context.

We find it meaningful for museums to track the presence, impact and the community around these artifacts worldwide and in the digital sphere?