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Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz

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3D Convolutional Neural Networks — A Reading List

Lifting convolutional neural networks to 3D data is challenging due to different data modalities (videos, image volumes, CAD models, LiDAR data etc.) as well as computational limitations (regarding runtime and memory). In this article, I want to summarize several recent papers addressing these problems and tackling different applications such as shape recognition, shape retrieval, medical image segmentation or object detection.

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Notes on Goodfellow’s “Deep Learning” Textbook

In this article, I collect my notes on Ian Goodfellow’s Deep Learning textbook. While the basic chapters are not included, the notes can be understood as short summaries of the corresponding chapters.

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SERIES»Working with Ubuntu«

LAPACKE_H_PATH-NOTFOUND when Installing OpenCV and Netbeans Links

This time in “Working with Ubuntu”: solving LAPACKE_H_PATH-NOTFOUND when installing OpenCV and some useful Netbeans plugins.

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Visualizing Triangular Meshes from .off Files using Python occmodel

This article discusses how to visualize triangular meshes available in Object File Format (.off) in Python using occmodel. Installation instructions for installing occmodel on Ubuntu are included.

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SERIES»Working with Ubuntu«

Monitoring GPU Usage, CUDA_VISIBLE_DEVICES Issues and HDF5 for Torch

This series collects every-day problems when doing research and development with Ubuntu; this time: comfortably monitoring GPU usage, clarifying issues with CUDA_VISIBLE_DEVICES and HDF5 for Torch.

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Reviews and Rebuttal for “Superpixels: An Evaluation of the State-of-the-Art”

This article summarizes the reviews corresponding to our paper “Superpixels: An Evaluation of the State-of-the-Art”. The paper was accepted for publication in Computer Vision and Image Understanding. The reviews correspond to v2 on ArXiv. The updated version will be made available on ArXiv.

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ARTICLE

A Note on Extending Tensorflow, PyTorch and Theano for Deep Learning on Custom DataStructures

Many recent deep learning frameworks such as Tensorflow, PyTorch, Theano or Torch are based on dense tensors. However, deep learning on non-tensor data structures is also interesting – especially for sparse, three-dimensional data. This article summarizes some of my experiences regarding deep learning on custom data structures in the mentioned libraries.

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Examples for Getting Started with Torch for Deep Learning

This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.

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SERIES»Working with Ubuntu«

Building Tensorflow in an NFS-mounted $HOME, CUDA 8.0 Without Manual Driver Installation

This blog series collects useful insights for working with Ubuntu. In this article, I describe how to avoid building problems when building Tensorflow on an NFS-mounted $HOME directory and how to install CUDA 8.0 without a manual driver installation.

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Inspecting Tensorflow’s Tensors using C++ and Bazel

Currently it is difficult to successfully link C++ projects with Tensorflow. However, to compile and run smaller code snippets based on Tensorflow, it might be convenient to put the code inside the tensorflow code base and compile an individual executable using Bazel.

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