DeLiGAN - This project is an implementation of the Generative Adversarial Network

Overview

DeLiGAN

alt text

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. Via this project, we make two contributions:

  1. We propose a simple but effective modification of the GAN framework for settings where training data is diverse yet small in size.
  2. We propose a modification of inception-score proposed by Salimans et al. Our modified inception-score provides a single, unified measure of inter-class and intra-class variety in samples generated by a GAN.

Dependencies

The code for DeLiGAN is provided in Tensorflow 0.10 for the MNIST and Toy dataset, and in Theano 0.8.2 + Lasagne 0.2 for the CIFAR-10 and Sketches dataset. This code was tested on a Ubuntu 14.04 workstation hosting a NVIDIA Titan X GPU.

Datasets

This repository includes implementations for 4 different datasets.

  1. Toy (self generated unimodal and bimodal gaussians)
  2. MNIST (http://www.cs.toronto.edu/~gdahl/mnist.npz.gz)
  3. CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html)
  4. Sketches (http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/)

The models for evaluating DeLiGAN on these datasets can be found in our repo. The details for how to download and lay out the datasets can be found in src/datasets/README.md

Usage

Training DeLiGAN models

To run any of the models

  • First download the datasets and store them in the respective sub-folder of the datasets folder (src/datasets/)
  • To run the model on any of the datasets, go to the respective src folders and run the dg_'dataset'.py file in the respective dataset folders with two arguments namely, --data_dir and --results_dir. For example, starting from the top-level folder,
cd src/sketches 
python dg_sketches.py --data_dir ../datasets/sketches/ --results_dir ../results/sketches
  • Note that the results_dir needs to have 'train' as a sub-folder.

Modified inception score

For example, to obtain the modified inception scores on CIFAR

  • Download the inception-v3 model (http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz.) and store it in src/modified_inception_scores/cifar10/
  • Generate samples using the model trained in the dg_cifar.py and copy it to src/modified_inception_scores/cifar10/
  • Run transfer_cifar10_softmax_b1.py to transfer learn the last layer.
  • Perform the modifications detailed in the comments in transfer_cifar10_softmax_b1.py and re-run it to evaluate the inception scores.
  • The provided code can be modified slightly to work for sketches as well by following the comments provided in transfer_cifar10_softmax_b1.py

Parts of the code in this implementation have been borrowed from the Improved-GAN implementation by OpenAI (T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2226–2234, 2016.)

Cite

@inproceedings{DeLiGAN17,
  author = {Gurumurthy, Swaminathan and Sarvadevabhatla, Ravi Kiran and R. Venkatesh Babu},
  title = {DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data},
  booktitle = {Proceedings of the 2017 Conference on Computer Vision and Pattern Recognition},
  location = {Honolulu, Hawaii, USA}
 }

Q&A

Please send message to [email protected] if you have any query regarding the code.

Owner
Video Analytics Lab -- IISc
Developing intelligent systems for semantic understanding of image/video content.
Video Analytics Lab -- IISc
Pytorch implementation of RED-SDS (NeurIPS 2021).

Recurrent Explicit Duration Switching Dynamical Systems (RED-SDS) This repository contains a reference implementation of RED-SDS, a non-linear state s

Abdul Fatir 10 Dec 02, 2022
CCPD: a diverse and well-annotated dataset for license plate detection and recognition

CCPD (Chinese City Parking Dataset, ECCV) UPdate on 10/03/2019. CCPD Dataset is now updated. We are confident that images in subsets of CCPD is much m

detectRecog 1.8k Dec 30, 2022
βœ… How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

How Robust are Fact Checking Systems on Colloquial Claims? Official PyTorch implementation of our NAACL paper: Byeongchang Kim*, Hyunwoo Kim*, Seokhee

Byeongchang Kim 19 Mar 15, 2022
Perform zero-order Hankel Transform for an 1D array (float or real valued).

perform zero-order Hankel Transform for an 1D array (float or real valued). An discrete form of Parseval theorem is guaranteed. Suit for iterative problems.

1 Jan 17, 2022
Tf alloc - Simplication of GPU allocation for Tensorflow2

tf_alloc Simpliying GPU allocation for Tensorflow Developer: korkite (Junseo Ko)

Junseo Ko 3 Feb 10, 2022
Intelligent Video Analytics toolkit based on different inference backends.

English | δΈ­ζ–‡ OpenIVA OpenIVA is an end-to-end intelligent video analytics development toolkit based on different inference backends, designed to help

Quantum Liu 15 Oct 27, 2022
Official implementation of Pixel-Level Bijective Matching for Video Object Segmentation

BMVOS This is the official implementation of Pixel-Level Bijective Matching for Video Object Segmentation, to appear in WACV 2022. @article{cho2021pix

Suhwan Cho 13 Dec 14, 2022
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks

SalFBNet This repository includes Pytorch implementation for the following paper: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolu

12 Aug 12, 2022
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
CLIPImageClassifier wraps clip image model from transformers

CLIPImageClassifier CLIPImageClassifier wraps clip image model from transformers. CLIPImageClassifier is initialized with the argument classes, these

Jina AI 6 Sep 12, 2022
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
Pre-training of Graph Augmented Transformers for Medication Recommendation

G-Bert Pre-training of Graph Augmented Transformers for Medication Recommendation Intro G-Bert combined the power of Graph Neural Networks and BERT (B

101 Dec 27, 2022
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 01, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
Using VideoBERT to tackle video prediction

VideoBERT This repo reproduces the results of VideoBERT (https://arxiv.org/pdf/1904.01766.pdf). Inspiration was taken from https://github.com/MDSKUL/M

75 Dec 14, 2022
Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Real-time VIBE Inference VIBE frame-by-frame. Overview This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE]. Usage: i

23 Jul 02, 2022
RGB-stacking πŸ›‘ 🟩 πŸ”· for robotic manipulation

RGB-stacking πŸ›‘ 🟩 πŸ”· for robotic manipulation BLOG | PAPER | VIDEO Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes, Alex X. Lee*,

DeepMind 95 Dec 23, 2022
Pre-trained Deep Learning models and demos (high quality and extremely fast)

OpenVINOβ„’ Toolkit - Open Model Zoo repository This repository includes optimized deep learning models and a set of demos to expedite development of hi

OpenVINO Toolkit 3.4k Dec 31, 2022
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022