Adversarially Learned Inference

Related tags

Deep LearningALI
Overview

Adversarially Learned Inference

Code for the Adversarially Learned Inference paper.

Compiling the paper locally

From the repo's root directory,

$ cd papers
$ latexmk --pdf adverarially_learned_inference

Requirements

  • Blocks, development version
  • Fuel, development version

Setup

Clone the repository, then install with

$ pip install -e ALI

Downloading and converting the datasets

Set up your ~/.fuelrc file:

$ echo "data_path: \"<MY_DATA_PATH>\"" > ~/.fuelrc

Go to <MY_DATA_PATH>:

$ cd <MY_DATA_PATH>

Download the CIFAR-10 dataset:

$ fuel-download cifar10
$ fuel-convert cifar10
$ fuel-download cifar10 --clear

Download the SVHN format 2 dataset:

$ fuel-download svhn 2
$ fuel-convert svhn 2
$ fuel-download svhn 2 --clear

Download the CelebA dataset:

$ fuel-download celeba 64
$ fuel-convert celeba 64
$ fuel-download celeba 64 --clear

Training the models

Make sure you're in the repo's root directory.

CIFAR-10

$ THEANORC=theanorc python experiments/ali_cifar10.py

SVHN

$ THEANORC=theanorc python experiments/ali_svhn.py

CelebA

$ THEANORC=theanorc python experiments/ali_celeba.py

Toy task

$ THEANORC=theanorc python experiments/ali_mixture.py
$ THEANORC=theanorc python experiments/gan_mixture.py

Evaluating the models

Samples

$ THEANORC=theanorc scripts/sample [main_loop.tar]

e.g.

$ THEANORC=theanorc scripts/sample ali_cifar10.tar

Interpolations

$ THEANORC=theanorc scripts/interpolate [which_dataset] [main_loop.tar]

e.g.

$ THEANORC=theanorc scripts/interpolate celeba ali_celeba.tar

Reconstructions

$ THEANORC=theanorc scripts/reconstruct [which_dataset] [main_loop.tar]

e.g.

$ THEANORC=theanorc scripts/reconstruct cifar10 ali_cifar10.tar

Semi-supervised learning on SVHN

First, preprocess the SVHN dataset with the learned ALI features:

$ THEANORC=theanorc scripts/preprocess_representations [main_loop.tar] [save_path.hdf5]

e.g.

$ THEANORC=theanorc scripts/preprocess_representations ali_svhn.tar ali_svhn_preprocessed.hdf5

Then, launch the semi-supervised script:

$ python experiments/semi_supervised_svhn.py ali_svhn.tar [save_path.hdf5]

e.g.

$ python experiments/semi_supervised_svhn.py ali_svhn_preprocessed.hdf5

[...]
Validation error rate = ... +- ...
Test error rate = ... +- ...

Toy task

$ THEANORC=theanorc scripts/generate_mixture_plots [ali_main_loop.tar] [gan_main_loop.tar]

e.g.

$ THEANORC=theanorc scripts/generate_mixture_plots ali_mixture.tar gan_mixture.tar
Comments
  • Conditional Generation

    Conditional Generation

    I'm interested in getting the update to this codebase that includes the conditional generation, as covered in the more recent version of the paper (related image below). Can you let me know if that will be added to the repo? celeba_conditional_sequence

    opened by dribnet 8
  • mistake in D(x,z) input size

    mistake in D(x,z) input size

    In table 5 from the paper you state that the input size for D(x,z) is 1024x1x1 which I think it's wrong after looking at the previous output sizes D(x) and D(z). I think that should be 1536x1x1.

    Is that assumption correct?

    opened by edgarriba 5
  • deserialization of models hangs

    deserialization of models hangs

    Training goes well for me using the scripts in experiments with the latest version of blocks, but then when I run any subsequent command that uses the generated model like scripts/sample or scripts/reconstruct, the command hangs indefinitely. My guess is that the deserialization is getting jammed up.

    I can look into it more - not yet familiar with the new tar format - but curious if this might be a known issue.

    opened by dribnet 3
  • Fuel version problem

    Fuel version problem

    I installed the current development version of fuel, but had some issue in fuel downloading.

    $ fuel-download celeba 64 $ fuel-convert celeba 64 $ fuel-download celeba 64 --clear

    The error message I got is: fuel-download: error: unrecognized arguments: 64 if I remove 64, I got: TypeError: init() got an unexpected keyword argument 'max_value'

    Could someone please specify what version or commits of fuel and progressbar should I use? Thanks

    opened by hope-yao 1
  • Where to use the reparametrization trick

    Where to use the reparametrization trick

    In the decoder module. I found that z is sampled from N(0, 1), so where did you use the reparametrization trick described in formual (2) and (3) in the paper

    opened by wuhaozhe 0
  • semi-supervised learning

    semi-supervised learning

    Hello,I read the paper and the source code.And it mentioned 'The last three hidden layers of the encoder as well as its output are concatenated to form a 8960-dimensional feature vector.' in section 4.3 of the paper.Could you please tell me how to compute the dimension?Thanks very much

    opened by C-xiaomeng 1
  • ImportError: No module named ali.utils

    ImportError: No module named ali.utils

    I followed the same steps in the readme file, but when I run this line

    $ THEANORC=theanorc python experiments/ali_cifar10.py

    I get:

    Traceback (most recent call last):
  File "experiments/ali_cifar10.py", line 3, in <module>
    from ali.utils import get_log_odds, conv_brick, conv_transpose_brick, bn_brick
ImportError: No module named ali.utils
    opened by xtarx 0
  • Preprocess_representation has a bug for me

    Preprocess_representation has a bug for me

    Hi, I was trying to reproduce the representation learning results of paper. Everything works fine except "preprocess_representations" script. It is leading to this error:

    File "scripts/preprocess_representations", line 32, in preprocess_svhn bricks=[ali.encoder.layers[-9], ali.encoder.layers[-6], AttributeError: 'GaussianConditional' object has no attribute 'layers'

    Any help would be appreciated.

    opened by MarziEd 1
  • Semi-supervised learning

    Semi-supervised learning

    I've been trying to reproduce your figures for semi-supervised learning on CIFAR-10 (19.98% with 1000 labels). This result is based on the technique proposed in Salimans et al. (2016), not SVMs. Is there any way you can include your code, or at least any changes to the hyperparameters in ali_cifar10.py?

    Thanks in advance for your help.

    opened by christiancosgrove 7
Releases(v1)
Owner
Mohamed Ishmael Belghazi
Mohamed Ishmael Belghazi
GitHub Repository
The project page of paper: Architecture disentanglement for deep neural networks [ICCV 2021, oral]

This is the project page for the paper: Architecture Disentanglement for Deep Neural Networks, Jie Hu, Liujuan Cao, Tong Tong, Ye Qixiang, ShengChuan

Jie Hu 15 Aug 30, 2022
Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI) Preparation Clone the Synchronized-BatchNorm-P

Fangneng Zhan 12 Aug 10, 2022
This is the repository of our article published on MDPI Entropy "Feature Selection for Recommender Systems with Quantum Computing".

Collaborative-driven Quantum Feature Selection This repository was developed by Riccardo Nembrini, PhD student at Politecnico di Milano. See the websi

Quantum Computing Lab @ Politecnico di Milano 10 Apr 21, 2022
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
Breast cancer is been classified into benign tumour and malignant tumour.

Breast cancer is been classified into benign tumour and malignant tumour. Logistic regression is applied in this model.

1 Feb 04, 2022
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 04, 2022
PyVideoAI: Action Recognition Framework

This reposity contains official implementation of: Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognitio

Kiyoon Kim 22 Dec 29, 2022
Official implementation of VQ-Diffusion

Vector Quantized Diffusion Model for Text-to-Image Synthesis Overview This is the official repo for the paper: [Vector Quantized Diffusion Model for T

Microsoft 592 Jan 03, 2023
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel This repository is the official PyTorch implementation of BSRDM w

Zongsheng Yue 69 Jan 05, 2023
Pixray is an image generation system

Pixray is an image generation system

pixray 883 Jan 07, 2023
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Bitcoin Realized Volatility Forecasting with GARCH and Multivariate LSTM Author: Chi Bui This Repository Repository Directory ├── README.md

Chi Bui 113 Dec 29, 2022
A Transformer-Based Siamese Network for Change Detection

ChangeFormer: A Transformer-Based Siamese Network for Change Detection (Under review at IGARSS-2022) Wele Gedara Chaminda Bandara, Vishal M. Patel Her

Wele Gedara Chaminda Bandara 214 Dec 29, 2022
Fine-tune pretrained Convolutional Neural Networks with PyTorch

Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A

Alex Parinov 694 Nov 23, 2022
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan 70 Dec 18, 2022
Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio

Eliahu Horwitz 55 Dec 14, 2022
Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation

NorCal Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation On Model Calibration for Long-Tailed Object Detec

Tai-Yu (Daniel) Pan 24 Dec 25, 2022
[NeurIPS 2021] ORL: Unsupervised Object-Level Representation Learning from Scene Images

Unsupervised Object-Level Representation Learning from Scene Images This repository contains the official PyTorch implementation of the ORL algorithm

Jiahao Xie 55 Dec 03, 2022
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022
Multi-Glimpse Network With Python

Multi-Glimpse Network Our code requires Python ≥ 3.8 Installation For example, venv + pip: $ python3 -m venv env $ source env/bin/activate (env) $ pyt

9 May 10, 2022
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

Nicola Dall'Asen 10 May 24, 2022