Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Overview
Code for running simulations for the paper

"Capacity of Group-invariant Linear Readouts from Equivariant Representations:
How Many Objects can be Linearly Classified Under All Possible Views?",

by Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, and Cengiz Pehlevan.


Note that the file models/vgg.py contains copyright statements for
the original authors and modifiers of the script.

The python packages used for the simulations are contained in
environment.yml (this may include extra packages that are not necessary).


To generate Figure 1, run

python manifold_plots.py

This script is fairly simple and self-explanatory.


To generate Figures 2 and 3, run

python plot_cnn_capacity.py

At the bottom of the plot_cnn_capacity.py script, the plotting function
is called for different panels. Comment out lines to generate specific
figures. This script searches for a match with sets of parameters defined
in cnn_capacity_params.py. To modify parameters used for simulations,
modify the dictionaries in cnn_capacity_params.py or define your own
parameter sets. For a description of different parameter options,
see the docstring for the function cnn_capacity.get_capacity.

The simulations take quite a lot of time to run, even
with parallelization. Also a word of warning that
the simulations take a lot of memory (~100GB for n_cores=5).
To speed things up and reduce memory usage, one can set
perceptron_style=efficient or pool_over_group=True, or reduce n_dichotomies.
One can also choose to set seeds to seeds = [3] in plot_cnn_capacity.py.


cnn_capacity_utils.py contains utility functions. The VGG model can be found
in models/vgg.py. The direct sum (aka "grid cell") convolutional network model
can be found in models/gridcellconv.py The code for generating datasets can be
found in datasets.py.


The code was modified and superficially refactored in preparation for
releasing to the public. The simulations haven't been thoroughly tested after
this refactoring so it's not 100% guaranteed that the code is correct (though
it doesn't appear to throw errors). Fingers crossed that everything works
the way it should.

The development of this code was supported by the Harvard Data Science Initiative.
You might also like...
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you

Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

NeuralTextures This is repository with inference code for paper "StylePeople: A Generative Model of Fullbody Human Avatars" (CVPR21). This code is for

On Generating Extended Summaries of Long Documents

ExtendedSumm This repository contains the implementation details and datasets used in On Generating Extended Summaries of Long Documents paper at the

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Contrastive Learning Inverts the Data Generating Process
Contrastive Learning Inverts the Data Generating Process

Official code to reproduce the results and data presented in the paper Contrastive Learning Inverts the Data Generating Process.

[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'
[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'

[CVPR2021] Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator Overview This is the entire codebase for the paper

CVPR 2021:
CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

ACE Please find the preliminary version published at BMVC 2020 in the folder BMVC_version, and its extended journal version in Journal_version. Datase

This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

3D-CVF This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object

Owner
Matthew Farrell
Postdoc at the Harvard John A Paulson School of Engineering and Applied Sciences
Matthew Farrell
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

João Fonseca 3 Jan 3, 2023
Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"

One2Set This repository contains the code for our ACL 2021 paper “One2Set: Generating Diverse Keyphrases as a Set”. Our implementation is built on the

Jiacheng Ye 63 Jan 5, 2023
Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

DTU Acoustic Technology Group 11 Dec 17, 2022
Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper

Gotta Go Fast When Generating Data with Score-Based Models This repo contains the official implementation for the paper Gotta Go Fast When Generating

Alexia Jolicoeur-Martineau 89 Nov 9, 2022
Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper

AnimeGAN - Deep Convolutional Generative Adverserial Network PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Lear

Rohit Kukreja 23 Jul 21, 2022
Official Repository for the ICCV 2021 paper "PixelSynth: Generating a 3D-Consistent Experience from a Single Image"

PixelSynth: Generating a 3D-Consistent Experience from a Single Image (ICCV 2021) Chris Rockwell, David F. Fouhey, and Justin Johnson [Project Website

Chris Rockwell 95 Nov 22, 2022
Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

TEMOS: TExt to MOtionS Generating diverse human motions from textual descriptions Description Official PyTorch implementation of the paper "TEMOS: Gen

Mathis Petrovich 187 Dec 27, 2022
Source code for "MusCaps: Generating Captions for Music Audio" (IJCNN 2021)

MusCaps: Generating Captions for Music Audio Ilaria Manco1 2, Emmanouil Benetos1, Elio Quinton2, Gyorgy Fazekas1 1 Queen Mary University of London, 2

Ilaria Manco 57 Dec 7, 2022
Code for generating a single image pretraining dataset

Single Image Pretraining of Visual Representations As shown in the paper A critical analysis of self-supervision, or what we can learn from a single i

Yuki M. Asano 12 Dec 19, 2022
Code for ACL 21: Generating Query Focused Summaries from Query-Free Resources

marge This repository releases the code for Generating Query Focused Summaries from Query-Free Resources. Please cite the following paper [bib] if you

Yumo Xu 28 Nov 10, 2022