Systems and method for transforming input data to palettes using neural networks

A method, comprising:

• configuring a neural network to receive input data to generate prediction data;
• generating a multi-step convolution comprising a plurality of convolution steps;
• generating a multi-step upsampling pathway comprising a plurality of upsampling steps, wherein the plurality of upsampling steps comprises an input to receive output data from a corresponding convolution pathway step;
• wherein, in response to receiving the input data, feature map output data is generated at the plurality of convolution steps, and at least one step of the plurality of upsampling steps utilizes at least the generated feature map data to generate prediction data;
• and
• configuring the neural network to:
• extract one or more features from the input data;
• and
• generate the prediction data based on the extracted one or more features.

A system, comprising:

• one or more devices;
• one or more processors coupled to the one or more devices;
• and
• a non-transitory computer-readable storage medium comprising a neural network configured to receive an input data to generate prediction data, the neural network comprising:
• a multi-step convolution pathway comprising a plurality of convolution steps;
• a multi-step upsampling pathway comprising a plurality of upsampling steps, wherein the plurality of upsampling steps comprises an input to receive output data from a corresponding convolution pathway step;
• and
• wherein, in response to receiving the input data, feature map output data is generated at the plurality of convolution steps, and at least one step of the plurality of upsampling steps utilizes at least the generated feature map data to generate prediction data;
• wherein the neural network is further configured to:
• extract one or more features from the input data;
• and
• generate the prediction data based on the extracted one or more features.