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
A Temporal Extension Library for PyTorch Geometric

Documentation | External Resources | Datasets PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric. The library

Benedek Rozemberczki 1.9k Jan 07, 2023
Everything's Talkin': Pareidolia Face Reenactment (CVPR2021)

Everything's Talkin': Pareidolia Face Reenactment (CVPR2021) Linsen Song, Wayne Wu, Chaoyou Fu, Chen Qian, Chen Change Loy, and Ran He [Paper], [Video

71 Dec 21, 2022
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est

Bae, Gwangbin 138 Dec 28, 2022
DC3: A Learning Method for Optimization with Hard Constraints

DC3: A learning method for optimization with hard constraints This repository is by Priya L. Donti, David Rolnick, and J. Zico Kolter and contains the

CMU Locus Lab 57 Dec 26, 2022
Fast Neural Representations for Direct Volume Rendering

Fast Neural Representations for Direct Volume Rendering Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann This repository contains the code and s

Sebastian Weiss 20 Dec 03, 2022
Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

VIN: Value Iteration Networks A quick thank you A few others have released amazing related work which helped inspire and improve my own implementation

Kent Sommer 297 Dec 26, 2022
TransMorph: Transformer for Medical Image Registration

TransMorph: Transformer for Medical Image Registration keywords: Vision Transformer, Swin Transformer, convolutional neural networks, image registrati

Junyu Chen 180 Jan 07, 2023
BASH - Biomechanical Animated Skinned Human

We developed a method animating a statistical 3D human model for biomechanical analysis to increase accessibility for non-experts, like patients, athletes, or designers.

Machine Learning and Data Analytics Lab FAU 66 Nov 19, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Müller 10 Dec 07, 2022
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
Convert Apple NeuralHash model for CSAM Detection to ONNX.

Apple NeuralHash is a perceptual hashing method for images based on neural networks. It can tolerate image resize and compression.

Asuhariet Ygvar 1.5k Dec 31, 2022
Learning Tracking Representations via Dual-Branch Fully Transformer Networks

Learning Tracking Representations via Dual-Branch Fully Transformer Networks DualTFR ⭐ We achieves the runner-ups for both VOT2021ST (short-term) and

phiphi 19 May 04, 2022
[SIGGRAPH Asia 2021] Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN

Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN [Paper] [Project Website] [Output resutls] Official Pytorch i

Badour AlBahar 215 Dec 17, 2022
Offical code for the paper: "Growing 3D Artefacts and Functional Machines with Neural Cellular Automata" https://arxiv.org/abs/2103.08737

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata Video of more results: https://www.youtube.com/watch?v=-EzztzKoPeo Requirem

Robotics Evolution and Art Lab 51 Jan 01, 2023
Torch implementation of SegNet and deconvolutional network

Torch implementation of SegNet and deconvolutional network

Fedor Chervinskii 5 Jul 17, 2020
RGBD-Net - This repository contains a pytorch lightning implementation for the 3DV 2021 RGBD-Net paper.

[3DV 2021] We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator

Phong Nguyen Ha 4 May 26, 2022
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts

t5-japanese Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts. The following is a list of models that

Kimio Kuramitsu 1 Dec 13, 2021