Encoder-Decoder Neural Network Latent Vector Layer Posterior Collapse
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Solution Overview
Problem
Variational autoencoders often experience posterior collapse during the learning process, leading to ineffective generation of new data samples due to forgetting input data features, as they fail to properly learn the distribution of input data.
Innovation Solution
A pre-trained artificial neural network model with an encoder-decoder structure is enhanced by inserting a latent vector layer between the encoder and decoder layers, which transforms the output of the encoder into a pooling vector, then into a latent variable vector, and uses this vector as input for the decoder through a projection matrix, performing cross attention to prevent posterior collapse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a variational autoencoder with encoder-decoder structure is used, then data generation capability is provided, but posterior collapse occurs causing loss of input data features
Solution Approach 1:
A latent vector layer is introduced as an intermediary component between the encoder and decoder. This latent vector layer receives the encoded representation from the encoder and transforms it into a form suitable for the decoder, preventing the direct connection that causes posterior collapse. The latent vector layer acts as a mediator that preserves information flow while enabling generative capability.
Solution Approach 2:
The traditional encoder-decoder structure is segmented by inserting the latent vector layer in between. This segmentation divides the information flow into distinct stages: encoding, latent transformation, and decoding. By separating these functions into distinct layers, the model can properly learn data distributions without the posterior collapse that occurs in direct encoder-decoder connections.
2Device complexity
If the encoder-decoder structure is used without latent vector layer, then model simplicity is maintained, but proper learning of input data distribution fails
Solution Approach 1:
The latent vector layer performs preliminary transformation of the encoded representation before it reaches the decoder. This preliminary action prepares the data in a suitable format for generation, ensuring that the decoder receives properly structured information. The latent vector layer pre-processes the encoded features to prevent information loss before decoding occurs.
Solution Approach 2:
The latent vector layer serves as a necessary intermediary that bridges the encoder and decoder. Rather than maintaining simple direct connection, the mediator layer ensures reliable information transfer by transforming representations appropriately. This intermediary structure is essential for achieving both simplicity and reliability in the generative model.
Data Source
AI summary
Disclosed is a method of generating data based on input data by using a pre-trained artificial neural network model having an encoder-decoder structure. In particular, according to the present disclosure, a computing device generates new data based on a probability distribution of input data by using a pre-trained artificial neural network model having an encoder-decoder structure, and the pre-trained artificial neural network model having the encoder-decoder structure corresponds to a pre-trained model in which a latent vector layer is included between an encoder layer and a decoder layer of the artificial neural network model.