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A Case for Conditioning

Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset

Context

In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.

To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.

It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.

Structure

This repository is divided into four major folders:

  • Images
  • models
  • Notebooks
  • Reports

You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.

References

The following resources were used as a starting point and were heavily modified for my uses and experiments.

https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae

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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" + '

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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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1 watching
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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('^' + ".*" + '

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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
About
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1 watching
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0 forks
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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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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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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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Repository files navigation
A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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, '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('^' + ".*" + '

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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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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");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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A Case for Conditioning
Results.jpg
Sample of images produced by the transposed convolutional GAN trained on the CIFAR-10 dataset
Context
In this repository you will find various materials associated with the semester long research project for CAP6610. In this project I wanted to examine if the conditional variations of the GAN and VAE were superior to their unconditional counterparts. I also wanted to see if the upscaling convolution was actually better than the transposed convolution.
To validate this I trained 8 models on the CIFAR-10 dataset: 4 Unconditioned GANs and VAEs with some consisting of uspcaling convolutions and others of transposed convolutions and 4 of the same GANs and VAEs but conditioned. To validate the results of this I utilized FID and IS.
It was found that the conditional variations of the VAE were significantly better than their non-conditioned counterparts but for GANs, it seemed conditioning made it worse. More research will need to be done to determine the cause of this but this offers a unique insight into the power of conditioning generative models.
Structure
This repository is divided into four major folders:
  • Images
  • models
  • Notebooks
  • Reports
You can find some sample output of each model in images, the trained weights of each model under models, and the notebooks used to train and evaluate the models under Notebooks. Reports as the name states contains all the reports I wrote and developed this semester for the project.
References
The following resources were used as a starting point and were heavily modified for my uses and experiments.
https://keras.io/examples/generative/conditional_gan/https://keras.io/examples/generative/vae/https://keras.io/examples/generative/dcgan_overriding_train_step/https://www.tensorflow.org/tutorials/generative/cvae
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