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Oct 31, 2024 | 
arxiv.org | Jose Ignacio |Sascha Kirch |Sergio Martin |Sergio Martín |Clara Pérez-Molina
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Sep 16, 2024 | 
towardsdatascience.com | Sascha Kirch
🐍 Towards Mamba State Space Models for Images, Videos and Time SeriesPart 4 — Towards Mamba State Space Models for Images, Videos and Time SeriesThis is part 4 of my new multi-part series 🐍 Towards Mamba State Space Models for Images, Videos and Time Series. The field of computer vision has seen incredible advances in recent years. One of the key enablers for this development has been undoubtedly the introduction of the Transformer.
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Sep 3, 2024 | 
towardsdatascience.com | Sascha Kirch
🐍 Towards Mamba State Space Models for Images, Videos and Time SeriesPart 3 — Towards Mamba State Space Models for Images, Videos and Time SeriesThis is part 3 of my new multi-part series 🐍 Towards Mamba State Space Models for Images, Videos and Time Series. Mamba, the model to be said to replace the mighty Transformer, has come a long way from the initial idea of using state space models (SSMs) in deep learning.
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Aug 22, 2024 | 
towardsdatascience.com | Sascha Kirch
🐍 Towards Mamba State Space Models for Images, Videos and Time SeriesPart 2 — Towards Mamba State Space Models for Images, Videos and Time SeriesThis is part 2 of my new multi-part series 🐍 Towards Mamba State Space Models for Images, Videos and Time Series. State space models, known to many disciplines of engineering for decades, are now making their debut in deep learning.
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Aug 14, 2024 | 
towardsdatascience.com | Sascha Kirch
This is part 1 of my new multi-part series 🐍 Towards Mamba State Space Models for Images, Videos and Time Series. Is Mamba all you need? Certainly, people have thought that for a long time of the Transformer architecture introduced by A. Vaswani et. al. in Attention is all you need back in 2017. And without any doubt, the transformer has revolutionized the field of deep learning over and over again.
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Mar 27, 2024 | 
towardsdatascience.com | Sascha Kirch
🚀Sascha’s Paper ClubDenoising Diffusion Probabilistic Models by J. Ho et. al. This walkthrough is about a paper that kicked off a new era of generative deep learning in computer vision and many other fields subsequently: the era of diffusion models. It’s titled “Denoising Diffusion Probabilistic Models” and it introduces a new framework known as DDPM, the abbreviation of the paper’s title.
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Mar 20, 2024 | 
towardsdatascience.com | Sascha Kirch
🚀Sascha’s Paper ClubDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data by L. Yang et. al. Monocular depth estimation, the prediction of distance in 3D space from a 2D image. The “ill posed and inherently ambiguous problem”, as stated in literally every paper on depth estimation, is a fundamental problem in computer vision and robotics. At the same time foundation models dominate the scene in deep learning based NLP and computer vision.
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Mar 14, 2024 | 
towardsdatascience.com | Sascha Kirch
A Hands-on Guide for PractitionersLast summer a non-deep learning method for novel view synthesis has entered the game: 3D Gaussian splattig. It is a method to represent a scene in 3D and to render images in real-time from any viewing direction. Some even say they are replacing NeRFs, the predominant method for novel view synthesis and implicit scene representation at that time. I think that is debatable since NeRFs are much more than image renderers.
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Sep 27, 2023 | 
towardsdatascience.com | Sascha Kirch
Paper Walkthrough: Emerging Properties in Self-Supervised Vision TransformersIt is an exciting decade for computer vision. Great successes from the natural language domain are transferred to the vision domain including the introduction of the ViT (vision transformer) and lately large-scale self-supervised pre-training techniques have made headlines under the name of foundation models.
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Sep 14, 2023 | 
towardsdatascience.com | Sascha Kirch
Paper Walkthrough — Segment AnythingToday’s paper walkthrough it is going to be visual! We will analyze Segment Anything, a paper by Meta’s AI research team that made headlines not only in the research community but also by all sorts of deep learning practitioners and advocates.
