Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present a novel perspective on the memorization phenomenon and propose a simple yet effective approach to mitigate it. We argue that memorization occurs because of an attraction basin in the denoising process which steers the diffusion trajectory towards a memorized image. However, this can be mitigated by guiding the diffusion trajectory away from the attraction basin by not applying classifier-free guidance until an ideal transition point occurs from which classifier-free guidance is applied. This leads to the generation of non-memorized images that are high in image quality and well-aligned with the conditioning mechanism. To further improve on this, we present a new guidance technique, opposite guidance, that escapes the attraction basin sooner in the denoising process. We demonstrate the existence of attraction basins in various scenarios in which memorization occurs, and we show that our proposed approach successfully mitigates memorization. Our codebase is publicly available at https://github.com/SonyResearch/mitigating_memorization.

Recently, Generative Adversarial Networks (GANs) have been successfully scaled to billion-scale large text-to-image datasets. However, training such models entails a high training cost, limiting some applications and research usage. To reduce the cost, one promising direction is the incorporation of pre-trained models. The existing method of utilizing pre-trained models for a generator significantly reduced the training cost compared with the other large-scale GANs, but we found the model loses the diversity of generation for a given prompt by a large margin. To build an efficient and high-fidelity text-to-image GAN without compromise, we propose to use two specialized discriminators with Slicing Adversarial Networks (SANs) adapted for text-to-image tasks. Our proposed model, called SCAD, shows a notable enhancement in diversity for a given prompt with better sample fidelity. We also propose to use a metric called Per-Prompt Diversity (PPD) to evaluate the diversity of text-to-image models quantitatively. SCAD achieved a zero-shot FID competitive with the latest large-scale GANs at two orders of magnitude less training cost.

Among the requirements elicitation activities, the stakeholder analysis is the main source of requirements. In this article, we propose a new model of data-driven stakeholder analysis, named SIG (Stakeholder Intention Graph), a semantic extension of property graph model that can represent the stakeholders' intentions and their relationships. To elicit the stakeholders' intentions from the speech data during meetings, we developed a system of structural analysis and SIG generation method from speech data. Based on the graph theory, we also propose an analysis methodology of stakeholders' intentions and their structure with both global and local graph analyses. We implemented a speech data-driven stakeholder analysis system on the graph database Neo4j. As the output, the analysis system automatically generates the stakeholder matrix from the speech data at the meetings. We applied the analysis method and system to the speech data of actual development meetings on the public service systems, and demonstrated the effectiveness of the proposed method.