The Design Space of Generative Models
- URL: http://arxiv.org/abs/2304.10547v1
- Date: Sat, 15 Apr 2023 23:34:23 GMT
- Title: The Design Space of Generative Models
- Authors: Meredith Ringel Morris, Carrie J. Cai, Jess Holbrook, Chinmay
Kulkarni, Michael Terry
- Abstract summary: Card et al.'s classic paper "The Design Space of Input Devices" established the value of design spaces as a tool for HCI analysis and invention.
We posit that developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices.
- Score: 34.03779721086144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Card et al.'s classic paper "The Design Space of Input Devices" established
the value of design spaces as a tool for HCI analysis and invention. We posit
that developing design spaces for emerging pre-trained, generative AI models is
necessary for supporting their integration into human-centered systems and
practices. We explore what it means to develop an AI model design space by
proposing two design spaces relating to generative AI models: the first
considers how HCI can impact generative models (i.e., interfaces for models)
and the second considers how generative models can impact HCI (i.e., models as
an HCI prototyping material).
Related papers
- Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization.
We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons.
Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
arXiv Detail & Related papers (2024-03-20T00:41:12Z) - Generative VS non-Generative Models in Engineering Shape Optimization [0.3749861135832073]
We compare the effectiveness and efficiency of generative and non-generative models in constructing design spaces.
Non-generative models generate robust latent spaces with none or significantly fewer invalid designs when compared to generative models.
arXiv Detail & Related papers (2024-02-13T15:45:20Z) - Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model
Reuse [59.500060790983994]
This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend.
ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference.
arXiv Detail & Related papers (2023-08-17T19:12:13Z) - Designing Novel Cognitive Diagnosis Models via Evolutionary
Multi-Objective Neural Architecture Search [13.9289351255891]
We propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS)
Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.
arXiv Detail & Related papers (2023-07-10T09:09:26Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Design of Unmanned Air Vehicles Using Transformer Surrogate Models [8.914156789222266]
We develop an AI Designer that synthesizes novel unmanned aerial vehicles (UAVs) designs.
Our approach uses a deep transformer model with a novel domain-specific encoding such that we can evaluate the performance of new proposed designs without running expensive flight dynamics models and CAD tools.
arXiv Detail & Related papers (2022-11-11T21:22:21Z) - Deep Generative Models in Engineering Design: A Review [1.933681537640272]
We present a review and analysis of Deep Generative Learning models in engineering design.
Recent DGMs have shown promising results in design applications like structural optimization, materials design, and shape synthesis.
arXiv Detail & Related papers (2021-10-21T02:50:10Z) - Dynamically Grown Generative Adversarial Networks [111.43128389995341]
We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation.
The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
arXiv Detail & Related papers (2021-06-16T01:25:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.