# Homogeneous Architecture Augmentation for Neural Predictor

arXiv: https://arxiv.org/abs/2107.13153 · HTML: https://mlsanity.com/paper/2107.13153/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [Neural Predictor for Neural Architecture Search](https://mlsanity.com/paper/1912.00848/) (2019)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [ReNAS:Relativistic Evaluation of Neural Architecture Search](https://mlsanity.com/paper/1910.01523/) (2019)
- [Very Deep Convolutional Networks for End-to-End Speech Recognition](https://mlsanity.com/paper/1610.03022/) (2016)
- [A Semi-Supervised Assessor of Neural Architectures](https://mlsanity.com/paper/2005.06821/) (2020)
- [Peephole: Predicting Network Performance Before Training](https://mlsanity.com/paper/1712.03351/) (2017)
- [A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification](https://mlsanity.com/paper/1712.05042/) (2017)

## Built on by (most cited first, 14 of 14)

- [NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction](https://mlsanity.com/paper/2211.08024/) (2022)
- [CAP: A Context-Aware Neural Predictor for NAS](https://mlsanity.com/paper/2406.02056/) (2024)
- [Automatic Graph Topology-Aware Transformer](https://mlsanity.com/paper/2405.19779/) (2024)
- [Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation](https://mlsanity.com/paper/2110.05242/) (2021)
- [PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud](https://mlsanity.com/paper/2211.15759/) (2022)
- [DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning](https://mlsanity.com/paper/2302.13020/) (2023)
- [CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor](https://mlsanity.com/paper/2506.04001/) (2025)
- [DAS: Neural Architecture Search via Distinguishing Activation Score](https://mlsanity.com/paper/2212.12132/) (2022)
- [Loss Functions for Predictor-based Neural Architecture Search](https://mlsanity.com/paper/2506.05869/) (2025)
- [Automating Neural Architecture Design without Search](https://mlsanity.com/paper/2204.11838/) (2022)
- [PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework](https://mlsanity.com/paper/2210.14460/) (2022)
- [FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search](https://mlsanity.com/paper/2404.15622/) (2024)
- [CSCO: Connectivity Search of Convolutional Operators](https://mlsanity.com/paper/2404.17152/) (2024)
- [From Regression to Inference: Meta-Learning Predictors for Neural Architecture Search](https://mlsanity.com/paper/2605.09290/) (2026)

## Abstract (arXiv)

Neural Architecture Search (NAS) can automatically design well-performed architectures of Deep Neural Networks (DNNs) for the tasks at hand. However, one bottleneck of NAS is the prohibitively computational cost largely due to the expensive performance evaluation. The neural predictors can directly estimate the performance without any training of the DNNs to be evaluated, thus have drawn increasing attention from researchers. Despite their popularity, they also suffer a severe limitation: the shortage of annotated DNN architectures for effectively training the neural predictors. In this paper, we proposed Homogeneous Architecture Augmentation for Neural Predictor (HAAP) of DNN architectures to address the issue aforementioned. Specifically, a homogeneous architecture augmentation algorithm is proposed in HAAP to generate sufficient training data taking the use of homogeneous representation. Furthermore, the one-hot encoding strategy is introduced into HAAP to make the representation of DNN architectures more effective. The experiments have been conducted on both NAS-Benchmark-101 and NAS-Bench-201 dataset. The experimental results demonstrate that the proposed HAAP algorithm outperforms the state of the arts compared, yet with much less training data. In addition, the ablation studies on both benchmark datasets have also shown the universality of the homogeneous architecture augmentation.