This article provides an overview of the paper “Adversarial Generation of Continuous Implicit Shape Representations”, which I co-authored with Matthias Fey. While the paper focuses on the theoretical aspects, I’ll provide a higher level explanation and and some visualizations here on the blog.
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Click here for the interactive visualization.
The Natural History Museum in London has a data portal in which they provide digital records for many of their specimens. Some of these records have images. I recently learned how to use machine learning tools such as convolutional neural networks and I wanted to use the NHM data to see what can be done with these tools. The dataset of butterflies seemed particularly interesting to me because the images are visually interesting, yet they are all similar in that they all contain a butterfly in a canonical pose.
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I wrote two programs that run on my LED matrix. They have different approaches and different aims. This post describes one of them.
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This is a 16×16 RGB LED matrix, made of 256 WS2812B LEDs. It’s powered by a Raspberry Pi and can display images and animations. With a game controller attached, it can play games.
continue readingMy fishtank is now internet-connected. It is run by a Raspberry Pi that can do three things: Feed the fish, switch the lights and take pictures.
To feed the fish, the Raspberry Pi sends commands to an automatic fishfeeder that I modified. It can empty any container in any order. This is achieved by an Arduino Pro Mini, two servos and a motor (more). The plate prevents hot air from flowing into the feeder. In an early iteration of the project, this made the food sticky and kept it from falling into the tank. Switching the lights is done using a remote light switch and 433Mhz transmitter. Finally, there is a webcam that is connected directly to the Raspberry Pi to take pictures.
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