Summary of Pomonag: Pareto-optimal Many-objective Neural Architecture Generator, by Eugenio Lomurno et al.
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
by Eugenio Lomurno, Samuele Mariani, Matteo Monti, Matteo Matteucci
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to Neural Architecture Search (NAS) that addresses the limitations of existing methods by considering multiple objectives simultaneously. Unlike previous state-of-the-art method DiffusionNAG, which only optimizes for accuracy, this paper’s Pareto-Optimal Many-Objective Neural Architecture Generator (POMONAG) considers factors like model complexity, computational efficiency, and inference latency, which are crucial for deploying models in resource-constrained environments. To achieve this, POMONAG uses a many-objective diffusion process that integrates Performance Predictor models to estimate these metrics and guide diffusion gradients. The proposed approach is evaluated on two search spaces (NASBench201 and MobileNetV3) and 15 image classification datasets, demonstrating improved performance and efficiency compared to the previous state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POMONAG is a new way of designing neural networks that doesn’t just focus on making them good at one thing. It considers many different things at once, like how simple or complicated the network is, how much computer power it needs, and how fast it can make predictions. This makes it better for using in real-world situations where resources are limited. The approach uses a special kind of process called diffusion to find the best balance between these different objectives. |
Keywords
» Artificial intelligence » Diffusion » Image classification » Inference
