This paper presents a new domain-specific representation learning method, exponential dissimilarity-dispersion family (EDDF), a novel distribution family that includes a dissimilarity function and a global dispersion parameter. In generative models, variational autoencoders (VAEs) has a solid theoretical foundation based on variational inference in visual representation learning and are also used as one of core components of other generative models. This paper addresses the issue where conventional VAEs, with the commonly adopted Gaussian settings, tend to experience performance degradation in generative modeling for high-dimensional data. This degradation is often caused by their excessively limited model family. To tackle this problem, we propose EDDF, a new domain-specific method introducing a novel distribution family with a dissimilarity function and a global dispersion parameter. A decoder using this family employs dissimilarity functions for the evidence lower bound (ELBO) reconstruction loss, leveraging domain-specific knowledge to enhance high-dimensional data modeling. We also propose an ELBO optimization method for VAEs with EDDF decoders that implicitly approximates the stochastic gradient of the normalizing constant using log-expected dissimilarity. Empirical evaluations of the generative performance show the effectiveness of our model family and proposed method. Our framework can be integrated into any VAE-based generative models in representation learning. The code and model are available at https://github.com/ganmodokix/eddf-vae.

Disentangled representation learning aims at obtaining an independent latent representation without supervisory signals. However, the independence of a representation does not guarantee interpretability to match human intuition in the unsupervised settings. In this article, we introduce conceptual representation learning, an unsupervised strategy to learn a representation and its concepts. An antonym pair forms a concept, which determines the semantically meaningful axes in the latent space. Since the connection between signifying words and signified notions is arbitrary in natural languages, the verbalization of data features makes the representation make sense to humans. We thus construct Conceptual VAE (ConcVAE), a variational autoencoder (VAE)-based generative model with an explicit process in which the semantic representation of data is generated via trainable concepts. In visual data, ConcVAE utilizes natural language arbitrariness as an inductive bias of unsupervised learning by using a vision-language pretraining, which can tell an unsupervised model what makes sense to humans. Qualitative and quantitative evaluations show that the conceptual inductive bias in ConcVAE effectively disentangles the latent representation in a sense-making manner without supervision. Code is available at https://github.com/ganmodokix/concvae.

Unsupervised learning of disentangled representations is a core task for discovering interpretable factors of variation in an image dataset. We propose a novel method that can learn disentangled representations with semantic explanations on natural image datasets. In our method, we guide the representation learning of a variational autoencoder (VAE) via reconstruction in a visual-semantic embedding (VSE) space to leverage the semantic information of image data and explain the learned latent representations in an unsupervised manner. We introduce a semantic sub-encoder and a linear semantic sub-decoder to learn word vectors corresponding to the latent variables to explain factors of variation in the language form. Each basis vector (column) of the linear semantic sub-decoder corresponds to each latent variable, and we can interpret the basis vectors as word vectors indicating the meanings of the latent representations. By introducing the sub-encoder and the sub-decoder, our model can learn latent representations that are not just disentangled but interpretable. Comparing with other state-of-the-art unsupervised disentangled representation learning methods, we observe significant improvements in the disentanglement and the transferability of latent representations.