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Found 7 papers, 1 papers with code
3D-FM GAN: Towards 3D-Controllable Face Manipulation
While concatenating GAN inversion and a 3D-aware, noise-to-image GAN is a straight-forward solution, it is inefficient and may lead to noticeable drop in editing quality.
Evolving Transferable Neural Pruning Functions
While most prior works in evolutionary learning aim at directly searching the structure of a network, few attempts have been made on another promising track, channel pruning, which recently has made major headway in designing efficient deep learning models.
Class-Discriminative CNN Compression
We then propose a novel layer-adaptive hierarchical pruning approach, where we use a coarse class discrimination scheme for early layers and a fine one for later layers.
A compressive multi-kernel method for privacy-preserving machine learning
Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors.
Content-Aware GAN Compression
We then propose a novel content-aware method to guide the processes of both pruning and distillation.
Rethinking Class-Discrimination Based CNN Channel Pruning
To this end, we initiate the first study on the effectiveness of a broad range of discriminant functions on channel pruning.
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
The quest for better data analysis and artificial intelligence has lead to more and more data being collected and stored.
