Interactive dimensionality reduction for large datasets

Related tags

Deep Learningblossom
Overview

BlosSOM 🌼

BlosSOM is a graphical environment for running semi-supervised dimensionality reduction with EmbedSOM. You can use it to explore multidimensional datasets, and produce great-looking 2-dimensional visualizations.

WARNING: BlosSOM is still under development, some stuff may not work right, but things will magically improve without notice. Feel free to open an issue if something looks wrong.

screenshot

BlosSOM was developed at the MFF UK Prague, in cooperation with IOCB Prague.

MFF logoIOCB logo

Overview

BlosSOM creates a landmark-based model of the dataset, and dynamically projects all dataset point to your screen (using EmbedSOM). Several other algorithms and tools are provided to manage the landmarks; a quick overview follows:

  • High-dimensional landmark positioning:
    • Self-organizing maps
    • k-Means
  • 2D landmark positioning
    • k-NN graph generation (only adds edges, not vertices)
    • force-based graph layouting
    • dynamic t-SNE
  • Dimensionality reduction
    • EmbedSOM
    • CUDA EmbedSOM (with roughly 500x speedup, enabling smooth display of a few millions of points)
  • Manual landmark position optimization
  • Visualization settings (colors, transparencies, cluster coloring, ...)
  • Dataset transformations and dimension scaling
  • Import from matrix-like data files
    • FCS3.0 (Flow Cytometry Standard files)
    • TSV (Tab-separated CSV)
  • Export of the data for plotting

Compiling and running BlosSOM

You will need cmake build system and SDL2.

For CUDA EmbedSOM to work, you need the NVIDIA CUDA toolkit. Append -DBUILD_CUDA=1 to cmake options to enable the CUDA version.

Windows (Visual Studio 2019)

Dependencies

The project requires SDL2 as an external dependency:

  1. install vcpkg tool and remember your vcpkg directory
  2. install SDL: vcpkg install SDL2:x64-windows

Compilation

git submodule init
git submodule update

mkdir build
cd build

# You need to fix the path to vcpkg in the following command:
cmake .. -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE="Release" -DCMAKE_INSTALL_PREFIX=./inst -DCMAKE_TOOLCHAIN_FILE=your-vcpkg-clone-directory/scripts/buildsystems/vcpkg.cmake

cmake --build . --config Release
cmake --install . --config Release

Running

Open Visual Studio solution BlosSOM.sln, set blossom as startup project, set configuration to Release and run the project.

Linux (and possibly other unix-like systems)

Dependencies

The project requires SDL2 as an external dependency. Install libsdl2-dev (on Debian-based systems) or SDL2-devel (on Red Hat-based systems), or similar (depending on the Linux distribution). You should be able to install cmake package the same way.

Compilation

git submodule init
git submodule update

mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=./inst    # or any other directory
make install                              # use -j option to speed up the build

Running

./inst/bin/blossom

Documentation

Quickstart

  1. Click on the "plus" button on the bottom right side of the window
  2. Choose Open file (the first button from the top) and open a file from the demo_data/ directory
  3. You can now add and delete landmarks using ctrl+mouse click, and drag them around.
  4. Use the tools and settings available under the "plus" button to optimize the landmark positions and get a better visualization.

See the HOWTO for more details and hints.

Performance and CUDA

If you pass -DBUILD_CUDA=1 to the cmake commands, you will get extra executable called blossom_cuda (or blossom_cuda.exe, on Windows).

The 2 versions of BlosSOM executable differ mainly in the performance of EmbedSOM projection, which is more than 100× faster on GPUs than on CPUs. If the dataset gets large, only a fixed-size slice of the dataset gets processed each frame (e.g., at most 1000 points in case of CPU) to keep the framerate in a usable range. The defaults in BlosSOM should work smoothly for many use-cases (defaulting at 1k points per frame on CPU and 50k points per frame on GPU).

If required (e.g., if you have a really fast GPU), you may modify the constants in the corresponding source files, around the call sites of clean_range(), which is the function that manages the round-robin refreshing of the data. Functionality that dynamically chooses the best data-crunching rate is being implemented and should be available soon.

License

BlosSOM is licensed under GPLv3 or later. Several small libraries bundled in the repository are licensed with MIT-style licenses.

Repository for the paper "PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation", CVPR 2021.

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation Code repository for the paper: PoseAug: A Differentiable Pose Augme

Pyjcsx 328 Dec 17, 2022
Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset Description This dataset contains the six different classes as described in our paper[]. The

0 Dec 16, 2021
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction This is the code for the paper Combining E

Robotics and Perception Group 69 Dec 26, 2022
Differentiable Simulation of Soft Multi-body Systems

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

YilingQiao 26 Dec 23, 2022
This is the code related to "Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation" (ICCV 2021).

Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation This is the code relat

39 Sep 23, 2022
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由,在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 60 Nov 14, 2022
Hough Transform and Hough Line Transform Using OpenCV

Hough transform is a feature extraction method for detecting simple shapes such as circles, lines, etc in an image. Hough Transform and Hough Line Transform is implemented in OpenCV with two methods;

Happy N. Monday 3 Feb 15, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

deSpeckNet-TF-GEE This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling publi

Adugna Mullissa 16 Sep 07, 2022
Object Detection using YOLO from PyImageSearch

Object Detection using YOLO from PyImageSearch By applying object detection, you’ll not only be able to determine what is in an image, but also where

Mohamed NIANG 1 Feb 09, 2022
ICLR 2021: Pre-Training for Context Representation in Conversational Semantic Parsing

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing This repository contains code for the ICLR 2021 paper "SCoRE: Pre-Tr

Microsoft 28 Oct 02, 2022
The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"

SHGNN: Structure-Aware Heterogeneous Graph Neural Network The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural

Wentao Xu 7 Nov 13, 2022
Dist2Dec: A Simplicial Neural Network for Homology Localization

Dist2Dec: A Simplicial Neural Network for Homology Localization

Alexandros Keros 6 Jun 12, 2022
A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation.

TiSASRec.paddle A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation. Introduction 论文:Time Interval Aware Sel

Paddorch 2 Nov 28, 2021
FeTaQA: Free-form Table Question Answering

FeTaQA: Free-form Table Question Answering FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form

Language, Information, and Learning at Yale 40 Dec 13, 2022
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

Advanced Image Manipulation Lab @ Samsung AI Center Moscow 4.7k Dec 31, 2022
Bolt Online Learning Toolbox

Bolt Online Learning Toolbox Bolt features discriminative learning of linear predictors (e.g. SVM or Logistic Regression) using fast online learning a

Peter Prettenhofer 87 Dec 12, 2022
PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

mlp-mixer-pytorch PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021) Usage import torch from mlp_mixer

isaac 27 Jul 09, 2022
Spectralformer: Rethinking hyperspectral image classification with transformers

Spectralformer: Rethinking hyperspectral image classification with transformers Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza

Danfeng Hong 102 Dec 29, 2022