89 lines
4.1 KiB
Markdown
89 lines
4.1 KiB
Markdown
+++
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title = "Übersetzung: Coding Examples"
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date = 2021-03-01
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authors = ["Aron Petau"]
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description = "A selection of coding projects from my Bachelor's in Cognitive Science"
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[taxonomies]
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tags = [
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"AI",
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"CNN",
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"GOFAI",
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"MTCNN",
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"computer vision",
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"ethics",
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"face detection",
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"google colab",
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"jupyter notebook",
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"keras",
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"machine learning",
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"neural nets",
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"object recognition",
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"python",
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"super resolution",
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"tensorflow",
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"university of osnabrück"
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]
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[extra]
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banner = "sample_lr.png"
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show_copyright = true
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show_shares = true
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+++
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## Neural Networks and Computer Vision
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## A selection of coding projects
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Although pure coding and debugging are often not a passion of mine, I recognize the importance of neural networks and other recent developments in Computer Vision. From several projects regarding AI and Machine Learning that I co-authored during my Bachelor Program, I picked this one since I think it is well documented and explains on a step-by-step basis what we do there.
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### Image Super-Resolution using Convolutional Neural Networks (Recreation of a 2016 Paper)
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Image Super-Resolution is a hugely important topic in Computer Vision. If it works sufficiently advanced, we could take all our screenshots and selfies and cat pictures from the 2006 facebook-era and even from before and scale them up to suit modern 4K needs.
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Just to give an example of what is possible in 2020, just 4 years after the paper here, have a look at this video from 1902:
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{{ youtube(id="EQs5VxNPhzk") }}
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The 2016 paper we had a look at is much more modest: it tries to upscale only a single Image, but historically, it was one of the first to achieve computing times sufficiently small to make such realtime-video-upscaling as visible in the Video (from 2020) or of the likes that Nvidia uses nowadays to upscale Videogames.
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Example of a Super-Resolution Image.
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The Neural network is artificially adding Pixels so that we can finally put our measly selfie on a billboard poster and not be appalled by our deformed-and-pixelated-through-technology face.
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{% gallery() %}
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[
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{
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"file": "sample_lr.png",
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"title": "A low-resolution sample",
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"alt": "A sample image with low resolution, used as a baseline for comparison."
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},
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{
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"file": "sample_hr.png",
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"title": "A high-resolution sample. This is also called 'ground truth'",
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"alt": "A high-resolution image that serves as the reference ground truth for comparison with other samples."
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},
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{
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"file": "sample_sr.png",
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"title": "The artificially enlarged image patch resulting from the algorithm",
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"alt": "A sample image where the resolution has been artificially increased using an image enhancement algorithm."
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},
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{
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"file": "sample_loss.png",
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"title": "A graph showing an exemplary loss function applied during training",
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"alt": "A graph illustrating the loss function used to train the model, showing the model's performance over time."
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},
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{
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"file": "sample_cos_sim.png",
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"title": "One qualitative measurement we used was pixel-wise cosine similarity. It is used to measure how similar the output and the ground truth images are",
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"alt": "A visualization of pixel-wise cosine similarity, used to quantify how similar the generated image is to the ground truth image."
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}
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]
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{% end %}
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[The Python notebook for Image super-resolution in Colab]( https://colab.research.google.com/drive/1RlgIKJmX8Omz9CTktX7cdIV_BwarUFpv?usp=sharing)
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### MTCNN (Application and Comparison of a 2016 Paper)
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Here, you can also have a look at another, much smaller project, where we rebuilt a rather classical Machine learning approach for face detection. Here, we use preexisting libraries to demonstrate the difference in efficacy of approaches, showing that Multi-task Cascaded Convolutional Networks (MTCNN) was one of the best-performing approaches in 2016. Since I invested much more love and work into the above project, I would prefer for you to check that one out, in case two projects are too much.
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[Face detection using a classical AI Approach (Recreation of a 2016 Paper)](https://colab.research.google.com/drive/1uNGsVZ0Q42JRNa3BuI4W-JNJHaXD26bu?usp=sharing)
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