`LibMTL.architecture.CGC`¶

class CGC(task_name, encoder_class, decoders, rep_grad, multi_input, device, **kwargs)[source]¶

Bases: LibMTL.architecture.MMoE.MMoE

Customized Gate Control (CGC).

This method is proposed in Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations (ACM RecSys 2020 Best Paper) and implemented by us.

Parameters:

img_size (list) – The size of input data. For example, [3, 244, 244] denotes input images with size 3x224x224.
num_experts (list) – The numbers of experts shared by all the tasks and specific to each task, respectively. Each expert is an encoder network.

forward(inputs, task_name=None)[source]¶