This is a collection of several practice scripts for implementation of object detection, neural network compression, distillation, pruning and large models.
Details as follows:
Low Rank Approximations are used for few cases:
- To compress a Neural Network
- To denoise a signal
- To impute missing values/find a structure to fill empty data
Singular Value Decomposition (SVD) exists for every rectangular matrix. Here in '', gentle implementation of low rank SVD is scripted to compress a deep neural network.