SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
arXiv:2011.10684
Abstract
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists and continuing to optimize ELBO after this value will not improve inference accuracy. On the basis of the experiment results, we propose SHOT-VAE to address these problems without introducing additional prior knowledge. The SHOT-VAE offers two contributions: (1) A new ELBO approximation named smooth-ELBO that integrates the label predictive loss into ELBO. (2) An approximation based on optimal interpolation that breaks the ELBO value bottleneck by reducing the margin between ELBO and the data likelihood. The SHOT-VAE achieves good performance with a 25.30% error rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on CIFAR-10 with 4k labels.
12 pages, 6 figures, Accepted for presentation at AAAI2021
References in corpus (13)
- Categorical Reparameterization with Gumbel-Softmax
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Unsupervised Data Augmentation for Consistency Training
- Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
- Semi-Supervised Learning with Deep Generative Models
- Tutorial on Variational Autoencoders
- When Does Label Smoothing Help?
- MixMatch: A Holistic Approach to Semi-Supervised Learning
- Manifold Mixup: Better Representations by Interpolating Hidden States
- InfoVAE: Information Maximizing Variational Autoencoders
- Understanding disentangling in -VAE
- ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
- There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average