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Found 4 papers, 1 papers with code
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
However, the diverse patterns for coupling parameters, such as residual connections and group convolutions, the diverse deep learning frameworks, and the various time stages at which pruning can be performed make existing pruning methods less adaptable to different architectures, frameworks, and pruning criteria.
3D Labeling Tool
Training and testing supervised object detection models require a large collection of images with ground truth labels.
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world.
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasing attention recently.
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