Generative Models of Visually Grounded Imagination (1705.10762v8)

Abstract: It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we show how we can modify variational auto-encoders to perform this task. Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way. We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C's). Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et.al. and the BiVCCA method of Wang et.al.) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset.

Analysis of "Generative Models of Visually Grounded Imagination"

The paper "Generative Models of Visually Grounded Imagination" explores the development of generative models capable of creating images from abstract and compositionally novel concepts. This research specifically enhances the variational autoencoder (VAE) framework to facilitate a task referred to as visually grounded imagination—imagining novel visual concepts from incomplete or abstract descriptions.

Methodology and Technical Contributions

The authors propose a series of modifications to traditional VAEs to address the challenge of visually grounded imagination:

1. Multi-modal VAE Framework: They extend the VAE framework to a multi-modal setting, modeling both images and attribute vectors. The joint generative model is represented as $p(x, y, z) = p(z) p(x|z) p(y|z)$, linking latent variables with observable data in a unified model.
2. TELBO Objective Function: A novel training objective termed 'TELBO' (Triple ELBO) is introduced, which enhances latent space embeddings from multi-modal data and unpaired inputs, permitting shared latent representation for images and descriptions.
3. Product-of-Experts (POE) Inference Network: To accommodate abstract concepts with partially observed attributes, a POE model is introduced, where the cumulative specificity of the concept increases with more observed attributes, enabling the creation of a diverse image set for generalized concepts.
4. Evaluation Metrics - The 3 C's: Evaluation of generated images is conducted using three criteria—correctness, coverage, and compositionality. These criteria measure how well a generated set reflects the specified attributes, the diversity it exhibits across unspecified attributes, and its ability to handle novel attribute combinations not seen during training.

Experimental Results

Experiments were carried out on two datasets: MNIST-with-attributes (MNIST-A) and CelebA. Notably, the proposed method outperformed existing joint image-attribute VAE methods, such as JMVAE and BiVCCA, in various tasks:

• MNIST-A Dataset: It demonstrated favorable results in terms of correctness and coverage even for abstract or novel attribute configurations, highlighting its robustness in generalizing complex visual concepts.
• CelebA Dataset: The model showed the ability to accurately generate images based on attribute combinations that hadn't been seen during training—a testament to the model's compositional generalization prowess.

Implications and Future Prospects

The research has substantial implications for machine learning and artificial intelligence, particularly in advancing models that can generate realistic images from incomplete descriptions. Practically, this extends the viability of generative models in applications such as design prototyping, digital content creation, and aiding visually impaired systems with description-based task completion. Theoretically, it pushes the boundary on understanding abstract concept representation and the neural capacity to model intricate cognitive tasks akin to human imagination.

Future directions for this work involve integrating richer forms of data beyond attribute vectors, such as natural language, and extending the framework to understand scenes with multiple objects, thus enhancing the model’s utility in complex real-world applications.

In summary, the paper "Generative Models of Visually Grounded Imagination" proposes significant advancements in modifying VAE frameworks to handle abstract and unsupervised data effectively, paving the way for future developments in generative models with a refined understanding of abstraction and imagination.