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Robust Neural Networks

This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.

Implemented network architectures include:

This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.

Installation for Development

To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.

 ./install.sh

This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.

A Note on Dependencies

All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.

Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.

Running an Example

Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:

source venv/bin/activate
python examples/sandwich_mnist.py

Contact

Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.

About

JAX implementations of robust neural networks from the ACFR.

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Readme
MIT license
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2 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
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Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
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Used by
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
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Used by
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Robust Neural Networks
This repository contains a collection or robust neural network architectures developed at the Australian Centre For Robotics (ACFR). All networks are implemented in Python/JAX.
Implemented network architectures include:
This repository is a work-in-progress. More network architectures, tutorials, and documentation will be added as we go along.
Installation for Development
To install the required dependencies, open a terminal in the root directory of this repository and enter the following commands.
 ./install.sh
This will create a Python virtual environment at ./venv and install all dependencies. If you would rather create a virtual environment with conda, poetry, or something else, feel free to modify the install.sh script.
A Note on Dependencies
All code was tested and developed in Ubuntu 22.04 with CUDA 12.4 and Python 3.10.12.
Requirements were generated with pipreqs. The install.sh will check for whether CUDA is available for your machine, and install the corresponding jax package.
Running an Example
Once you have installed the package as above, simply activate the virtual environment and run any of the scripts in the examples/ folder. For example, from the root directory of the project, run:
source venv/bin/activate
python examples/sandwich_mnist.py
Contact
Please contact Nicholas Barbara (nicholas.barbara@sydney.edu.au) with any questions.
About
JAX implementations of robust neural networks from the ACFR.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages