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SOTA
Network Pruning
Network Pruning On Cifar 100
Network Pruning On Cifar 100
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Dense
79
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
AC/DC
78.2
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
Beta-Rank
74.01
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis
TAS-pruned ResNet-110
73.16
Network Pruning via Transformable Architecture Search
+U-DML*
-
PP-StructureV2: A Stronger Document Analysis System
0 of 5 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Network Pruning
Network Pruning On Cifar 100
Network Pruning On Cifar 100
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Dense
79
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
AC/DC
78.2
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
Beta-Rank
74.01
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis
TAS-pruned ResNet-110
73.16
Network Pruning via Transformable Architecture Search
+U-DML*
-
PP-StructureV2: A Stronger Document Analysis System
0 of 5 row(s) selected.
Previous
Next
Network Pruning On Cifar 100 | SOTA | HyperAI