Data Phoenix Digest - ISSUE 1.2023

Data Phoenix Digest - ISSUE 1.2023

ARTICLES

Dealing with DICOM Using ImageIO Python Package

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In this article, the author demonstrates how to use the ImageIO Python package to read DICOM files, extract metadata and attributes, and plot image slices using interactive slider widgets using Ipywidgets. Learn more about this new approach!

Building a GitOps ML Model Registry with DVC and GTO

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For this tutorial, the authors pick a simple project with no models registered yet, to demonstrate adding a model registry on top of an existing ML project. They show how to register semantic model versions, assign stages to them, and employ CI/CD, all using a GitOps approach.

10 Metrics to Evaluate Supervised Machine Learning Models

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The evaluation of models is usually an iterative process where there is a feedback loop between the results and model. This article look into ten major metrics that you can use to evaluate the performance of your machine learning models, step-by-step.

Scaling ML Model Development with MLflow

Model prototyping and experimenting are crucial parts of the model development journey. In this article, the author explores a possible Python SDK implementation to help DS teams to keep track of all the model’s experiments, saving from codes to artifacts to plots and related files.

PAPERS & PROJECTS

Scalable Diffusion Models with Transformers

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In this work, the researchers explore a new class of diffusion models based on the transformer architecture; train latent diffusion models, replacing the U-Net backbone with a transformer that operates on latent patches; and analyze the scalability of Diffusion Transformers (DiTs).

NeRF-Art: Text-Driven Neural Radiance Fields Stylization

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Neural radiance fields (NeRF) enable high-quality novel view synthesis. Editing NeRF, however, remains challenging. In this paper, the authors present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a single text prompt.

ECON: Explicit Clothed humans Obtained from Normals

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ECON combines the best aspects of implicit and explicit surfaces to infer high-fidelity 3D humans, even with loose clothing or in challenging poses. ECON is more accurate than the state of the art. Perceptual studies also show that ECON’s perceived realism is better by a large margin.

InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds

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InstantAvatar is a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an interactive rate. It converges 130x faster and can be trained in minutes instead of hours, way faster than competitors.


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