Dimensionality reduction of neural networks intermedia feature maps using two-dimensional principal component analysis

An apparatus comprising:

• at least one processor;
• and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:
• decode, from or along a bitstream, a mean matrix, a principal components matrix, and a row projection matrix;
• and reconstruct an original input matrix by adding the mean matrix to a product comprising a multiplication of the principal components matrix with a transpose of the row projection matrix;
• wherein the original input matrix corresponds to an input tensor, and the input tensor corresponds to at least one image.

An apparatus comprising:

• at least one processor;
• and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:
• determine at least one input tensor;
• determine an original input matrix that is a slice of the at least one input tensor along a channel dimension of the at least one input tensor, wherein the original input matrix has dimensions comprising at least a height and a width;
• determine a mean matrix corresponding to a mean of training data matrices, wherein the training data matrices are respective slices of the at least one input tensor along the channel dimension of the at least one input tensor;
• wherein the at least one input tensor corresponds to at least one input image;
• determine a row projection matrix as a concatenation of row projection vectors;
• determine a difference by subtracting the mean matrix from the original input matrix;
• determine a principal components matrix by multiplying the difference obtained by subtracting the mean matrix from the original input matrix with the row projection matrix;
• and encode the mean matrix, the principal components matrix, and the row projection matrix into or along a bitstream.