Hybrid Neural Codec Proxy Training

The patent combines pre-process and post-process neural networks into a unified training framework where both networks are trained simultaneously using a shared loss function. This merging allows the networks to learn complementary representations and improve their collaborative effectiveness in converting and reconstructing data, resolving the contradiction between independent training simplicity and joint training effectiveness.

The patent introduces a proxy codec as an intermediary that differentiates the training process from the actual encoding/decoding process. The proxy codec provides a differentiable approximation of the real codec during training, enabling gradient-based optimization. The real codec is then used for actual data processing. This intermediary resolves the contradiction by allowing versatile data type support through the real codec while maintaining training simplicity through the differentiable proxy.

The patent implements a feedback mechanism where the loss function continuously monitors the reconstruction quality and guides the training of neural networks. The loss function compares the original data with the reconstructed data and provides gradients that feed back into the training process. This feedback ensures that neural networks learn to maintain encoding efficiency while supporting versatile data formats, resolving the contradiction between adaptability and productivity.