An Interpretable Generative Model for Handwritten Digits Synthesis
By Yao Zhu, Saksham Suri, Pranav Kulkarni, Yueru Chen, Jiali Duan, C.-C. Jay Kuo. 2019
4 citations recorded by the source corpus.
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models such as the variational autoencoder (VAE) are trained by backpropagation (BP). The training process is complex, and its underlying mechanism is not transparent. Here, we present an explainable generative model using a feedforward design methodology without BP. Being similar to VAEs, it has an encoder and a decoder. For the encoder design, we derive principal-component-analysis-based (PCA-based) transform kernels using the covariance of its inputs. This process converts input images of correlated pixels to uncorrelated spectral components, which play the same role as latent variables in a VAE system. For the decoder design, we convert randomly generated spectral components to synthesized images through the inverse PCA transform. A subject test is conducted to compare the quality of digits generated using the proposed method and the VAE method. They offer comparable perceptual quality yet our model can be obtained at much lower complexity.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
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Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “For example [117] uses encoder-decoder system to perform multi-stage PCA.”