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Complex-Valued AutoEncoders for Object Discovery

We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations. After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.

This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.

Model figure

Setup

Make sure you have conda installed (you can find instructions here).

Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.

The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.

Run Experiments

To train and test the CAE, run one of the following commands, depending on the dataset you want to use:

python -m codebase.main +experiment=CAE_2Shapes

python -m codebase.main +experiment=CAE_3Shapes

python -m codebase.main +experiment=CAE_MNISTShapes

Citation

When using this code, please cite our paper:

@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact

For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.

About

Code for the paper: Complex-Valued Autoencoders for Object Discovery

arxiv.org/abs/2204.02075

Topics

deep-learningmachine-learningobject-discoverypytorch

Resources

Readme
MIT license
Activity

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59 stars

Watchers

2 watching

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7 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
About
Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
Topics
deep-learningmachine-learningobject-discoverypytorch
Resources
Readme
MIT license
Activity
Stars
59 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Complex-Valued AutoEncoders for Object Discovery
We present the Complex AutoEncoder – an object discovery approach that takes inspiration from neuroscience to implement distributed object-centric representations.
After introducing complex-valued activations into a convolutional autoencoder, it learns to encode feature information in the activations’ magnitudes and object affiliation in their phase values.
This repo provides a reference implementation for the Complex AutoEncoder (CAE) as introduced in our paper "Complex-Valued AutoEncoders for Object Discovery" (https://arxiv.org/abs/2204.02075) by Sindy Löwe, Phillip Lippe, Maja Rudolph and Max Welling.
Model figure
Setup
Make sure you have conda installed (you can find instructions here).
Then, run bash setup.sh to download the 2Shapes, 3Shapes and MNIST&Shapes datasets and to create a conda environment with all required packages.
The script installs PyTorch with CUDA 11.3, which is the version we used for our experiments. If you want to use a different version, you can change the version number in the setup.sh script.
Run Experiments
To train and test the CAE, run one of the following commands, depending on the dataset you want to use:
python -m codebase.main +experiment=CAE_2Shapes
python -m codebase.main +experiment=CAE_3Shapes
python -m codebase.main +experiment=CAE_MNISTShapes
Citation
When using this code, please cite our paper:
@article{lowe2022complex,
title={Complex-Valued Autoencoders for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Rudolph, Maja and Welling, Max},
journal={Transactions on Machine Learning Research (TMLR)},
year={2022}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.
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Code for the paper: Complex-Valued Autoencoders for Object Discovery
arxiv.org/abs/2204.02075
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deep-learningmachine-learningobject-discoverypytorch
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