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Found 4 papers, 3 papers with code
U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation
Semantic segmentation has witnessed remarkable advancements with the adaptation of the Transformer architecture.
Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy
As an attempt to solve this problem, pruning filters is a common solution, but most existing pruning methods do not preserve the model accuracy efficiently and therefore require a large number of finetuning epochs.
Toward Compact Deep Neural Networks via Energy-Aware Pruning
Despite the remarkable performance, modern deep neural networks are inevitably accompanied by a significant amount of computational cost for learning and deployment, which may be incompatible with their usage on edge devices.
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.
Explainable Artificial Intelligence (XAI)
Model Compression
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