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Found 5 papers, 2 papers with code
Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration
This study presents an efficient loss-aware automatic selection of structured pruning (LAASP) criteria for slimming and accelerating deep neural networks.
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural Networks
We studied several filter selection criteria based on filter magnitude and similarity among filters within a convolution layer, and based on the assumption that the sensitivity of each layer throughout the network is different, unlike conventional fixed rate pruning methods, our algorithm using loss-aware filter selection criteria automatically finds the suitable pruning rate for each layer throughout the network.
Recognition of facial expressions based on salient geometric features and support vector machines
We extract geometric features from point, line and triangle composed of tracking results of facial points.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Facial expression recognition based on local region specific features and support vector machines
But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions.
Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence.
