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RDP

Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186

Installation

The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.

Anomaly Detection

Data Preparation

Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.

Train

If you are under Dev mode (tweak it in train.py), just run

python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch

python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.

python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test

If you are under Dev mode, just run

python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch

python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.

python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering

Train

If you are under Dev mode (tweak it in train.py), just run

python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch

python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.

python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test

If you are under Dev mode, just run

python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch

python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.

python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About

Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186

Resources

Readme
Activity

Stars

30 stars

Watchers

2 watching

Forks

9 forks
Report repository

Releases

Packages

Contributors

Languages

, '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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Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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README.md
Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 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('^' + ".*" + '

Skip to content
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RDP-Clustering
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README.md
README.md
Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 forks
Report repository
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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
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7 Commits
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RDP-Anomaly
RDP-Clustering
RDP-Clustering
README.md
README.md
Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
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7 Commits
Folders and files
NameName
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RDP-Anomaly
RDP-Clustering
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README.md
README.md
Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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7 Commits
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README.md
README.md
Repository files navigation
RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

About
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Resources
Readme
Activity
Stars
30 stars
Watchers
2 watching
Forks
9 forks
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RDP
Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
Installation
The repo is tested on Ubuntu 16.04, Python 3.5.2, PyTorch 1.1.0 and Sklearn 0.21.1.
Anomaly Detection
Data Preparation
Some of example datasets are put in ./data folder due to the large file size limitation. You may downloaded them from the urls listed in the paper appendix.
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data/csv_file] [save_path] > [output_log] 2>&1 &

e.g.
python train.py data/apascal.csv save_model/apascal/ > logs/apascal.log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data/csv_file] [load_path] [tree_depth] > [output_log] 2>&1 &

e.g.
python test.py data/apascal.csv save_model/apascal/ 8 1 > logs/apascal_l8_test.log 2>&1 &
...

Clustering
Train
If you are under Dev mode (tweak it in train.py), just run
python train.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python train.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python train.py r8 192 512 > logs/r8_b192_[512].log 2>&1 &
...

Test
If you are under Dev mode, just run
python test.py

If you are under Server mode, the following scripts can be used to help you run experiments in batch
python test.py [data] [batch_size] [out_c] > [output_log] 2>&1 &

e.g.
python test.py r8 192 512 > logs/r8_b192_[512]_test.log 2>&1 &
...

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Codes for IJCAI2020 paper "Unsupervised Representation Learning by Predicting Random Distances” https://arxiv.org/abs/1912.12186
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