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Deep Feature Consistent Variational Autoencoder (1610.00291v2)

Published 2 Oct 2016 in cs.CV

Abstract: We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.

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Authors (4)
  1. Xianxu Hou (24 papers)
  2. Linlin Shen (133 papers)
  3. Ke Sun (136 papers)
  4. Guoping Qiu (61 papers)

Summary

Feature Perceptual Loss for Variational Autoencoder: An Overview

The paper introduces an innovative approach to enhancing the performance of Variational Autoencoders (VAEs) by utilizing feature perceptual loss, drawing from the successes of pretrained convolutional neural networks (CNNs) in style transfer applications. This methodology addresses the common issue of blurry image outputs in VAEs, traditionally reliant on pixel-based loss functions. By incorporating feature perceptual loss, the authors demonstrate improved image generation quality and latent space representation.

Main Contributions

The central contribution of this work is the integration of feature perceptual loss in training a VAE, which allows for a more semantically meaningful measure of similarity between the original and reconstructed images. This method capitalizes on the high-level perceptual features extracted from CNNs pretrained on large classification tasks such as ImageNet. Key contributions include:

  • Feature Perceptual Loss: Replaces traditional pixel-wise loss functions with differences in high-level feature representations. This approach underpins the enhanced image quality, capturing semantic and perceptual discrepancies rather than mere pixel differences.
  • Latent Space Exploration: The paper investigates the latent space's capability to encode semantic and conceptual information, facilitating smooth interpolation and meaningful attribute-specific manipulations.
  • Facial Attribute Prediction: The authors deploy the learned latent representations to predict facial attributes, showcasing competitive performance with established methods in the field of attribute detection.

Methodology

The proposed methodology leverages a VAE architecture augmented with a feature perceptual loss framework. This framework includes:

  1. Network Structure: The VAE is composed of encoder and decoder networks designed using deep CNN architectures similar to those found in popular models like VGGNet.
  2. Loss Computation: The reconstruction loss uses feature perceivable discrepancies obtained from pretrained CNN layers, bypassing the limitations of pixel-by-pixel similarity metrics that fail to reflect human perceptual understanding of images.
  3. Training Strategy: The total loss for training combines feature perceptual loss with the KL divergence, optimizing the balance between reconstruction accuracy and latent space regularization.

Experimental Results

The reported experiments are conducted on the CelebA dataset, focusing on face image generation and assessment. Results indicate:

  • Improved Image Quality: The VAE model trained with feature perceptual loss yields superior qualitative outcomes compared to standard VAEs and DCGANs, producing images with clearer details and more realistic features.
  • Latent Vector Interpolations: Demonstrated smooth transitions between latent representations, reinforcing the method's capability to capture semantically relevant information.
  • Attribute Manipulation and Prediction: The paper illustrates that the model allows for attribute-specific manipulations in image synthesis. Latent space features are shown to be effective in predicting facial attributes with high accuracy, excelling in specific attributes compared to other methods.

Implications and Future Work

By enhancing VAEs with feature perceptual loss, this research presents avenues for refining generative models to produce more visually authentic outputs. These advancements have significant implications:

  • Generative Model Refinements: This method offers a framework that can potentially be extended or combined with GAN methodologies to further enhance image generation realism.
  • Representation Learning: The methodology underscores the importance of semantically rich latent representations, beneficial for applications beyond simple image generation, such as conditional generation and other unsupervised learning tasks.
  • Further Research Directions: Given the success in image quality improvement, future work could explore integrating perceptual loss with other robust generative architectures or extending the feature loss to capture nuances not effectively modeled by current networks.

In conclusion, the paper presents a compelling case for the integration of feature perceptual loss into VAE training, enhancing the quality of generated images and showcasing the powerful representational capabilities of the latent spaces within VAEs. This method opens further research avenues in refining generative model output and understanding the encoding of semantic information in unsupervised learning contexts.

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