This paper describes InfoCatVAE, an extension of the variational autoencoder that enables unsupervised disentangled representation learning. InfoCatVAE uses multimodal distributions for the prior and the inference network and then maximizes the evidence lower bound objective (ELBO). We connect the new ELBO derived for our model with a natural soft clustering objective which explains the robustness of our approach. We then adapt the InfoGANs method to our setting in order to maximize the mutual information between the categorical code and the generated inputs and obtain an improved model.
Among the numerous studies that have been proposed about disentangled representation learning, a majority is based on a regularization along already existing objective functions for different generative models @cite_31 @cite_12 @cite_32 @cite_33 @cite_13 @cite_17 @cite_5 @cite_28 @cite_14 . Other studies focus on the clustering aspect of disentangling latent representation by exploring the effect of mixture distribution for latent representation in variational autoencoders @cite_18 @cite_9 @cite_6 @cite_3 . The mixture of distributions has the advantage of empowering the latent distribution to represent more complex variations in the data. Moreover, in term of disentangling, a mixture implicitly assumes that there is a hierarchical importance among meaningful factors of variations: each mode represents a major latent factor and local isotropic distributions hold minor disentangling power. Nevertheless, the mixture models hold several limits that depends on the chosen form of the prior and the form of the objective function.
@cite_18 @cite_14 @cite_33 @cite_28 @cite_9 @cite_32 @cite_6 @cite_3 @cite_5 @cite_31 @cite_13 @cite_12 @cite_17
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Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors. We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement. We evaluate the proposed approach using several quantitative metrics and empirically observe significant gains over existing methods in terms of both disentanglement and data likelihood (reconstruction quality). We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minimal supervision to learn a latent representation that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a collection of face images grouped by identity. We wish to anchor the semantics of the grouping into a relevant and disentangled representation that we can easily exploit. However, existing deep probabilistic models often assume that the observations are independent and identically distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of a set of grouped observations. The ML-VAE separates the latent representation into semantically meaningful parts by working both at the group level and the observation level, while retaining efficient test-time inference. Quantitative and qualitative evaluations show that the ML-VAE model (i) learns a semantically meaningful disentanglement of grouped data, (ii) enables manipulation of the latent representation, and (iii) generalises to unseen groups. Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we proposed a supervised algorithm called DNA-GAN trying to disentangle different attributes of images. The latent representations of images are DNA-like, in which each individual piece represents an independent factor of variation. By annihilating the recessive piece and swapping a certain piece of two latent representations, we obtain another two different representations which could be decoded into images. In order to obtain realistic images and also disentangled representations, we introduced the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets demonstrate the effectiveness of our method and the advantage of overcoming limitations existing in other methods. We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in @math -VAE, as training progresses. From these insights, we propose a modification to the training regime of @math -VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in @math -VAE, without the previous trade-off in reconstruction accuracy.