# ReNAS:Relativistic Evaluation of Neural Architecture Search

arXiv: https://arxiv.org/abs/1910.01523 · HTML: https://mlsanity.com/paper/1910.01523/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 30 of 37)

- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Contrastive Neural Architecture Search with Neural Architecture Comparators](https://mlsanity.com/paper/2103.05471/) (2021)
- [Dynamic Resolution Network](https://mlsanity.com/paper/2106.02898/) (2021)
- [Stronger NAS with Weaker Predictors](https://mlsanity.com/paper/2102.10490/) (2021)
- [Augmented Shortcuts for Vision Transformers](https://mlsanity.com/paper/2106.15941/) (2021)
- [Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?](https://mlsanity.com/paper/2203.14577/) (2022)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [Homogeneous Architecture Augmentation for Neural Predictor](https://mlsanity.com/paper/2107.13153/) (2021)
- [NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction](https://mlsanity.com/paper/2211.08024/) (2022)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search](https://mlsanity.com/paper/2204.05941/) (2022)
- [Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity](https://mlsanity.com/paper/2102.11382/) (2021)
- [GP-NAS-ensemble: a model for NAS Performance Prediction](https://mlsanity.com/paper/2301.09231/) (2023)
- [NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning](https://mlsanity.com/paper/2306.10792/) (2023)
- [Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search](https://mlsanity.com/paper/2201.12725/) (2022)
- [ParZC: Parametric Zero-Cost Proxies for Efficient NAS](https://mlsanity.com/paper/2402.02105/) (2024)
- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [Pareto-aware Neural Architecture Generation for Diverse Computational Budgets](https://mlsanity.com/paper/2210.07634/) (2022)
- [Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"](https://mlsanity.com/paper/2302.00932/) (2023)
- [DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning](https://mlsanity.com/paper/2302.13020/) (2023)
- [NN-Former: Rethinking Graph Structure in Neural Architecture Representation](https://mlsanity.com/paper/2507.00880/) (2025)
- [CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks](https://mlsanity.com/paper/2211.05950/) (2022)
- [CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor](https://mlsanity.com/paper/2506.04001/) (2025)
- [Across-Task Neural Architecture Search via Meta Learning](https://mlsanity.com/paper/2110.05842/) (2021)
- [NAAP-440 Dataset and Baseline for Neural Architecture Accuracy Prediction](https://mlsanity.com/paper/2209.06626/) (2022)
- [Loss Functions for Predictor-based Neural Architecture Search](https://mlsanity.com/paper/2506.05869/) (2025)
- [Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain](https://mlsanity.com/paper/2108.03001/) (2021)
- [WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor](https://mlsanity.com/paper/2203.02086/) (2022)
- [PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework](https://mlsanity.com/paper/2210.14460/) (2022)
- [Accuracy Prediction for NAS Acceleration using Feature Selection and Extrapolation](https://mlsanity.com/paper/2211.12419/) (2022)

## Abstract (arXiv)

An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing NAS algorithms often train and evaluate intermediate neural architectures on a small proxy dataset with limited training epochs. But it is difficult to expect an accurate performance estimation of an architecture in such a coarse evaluation way. This paper advocates a new neural architecture evaluation scheme, which aims to determine which architecture would perform better instead of accurately predict the absolute architecture performance. Therefore, we propose a relativistic architecture performance predictor in NAS (ReNAS). We encode neural architectures into feature tensors, and further refining the representations with the predictor. The proposed relativistic performance predictor can be deployed in discrete searching methods to search for the desired architectures without additional evaluation. Experimental results on NAS-Bench-101 dataset suggests that, sampling 424 (0.1% of the entire search space) neural architectures and their corresponding validation performance is already enough for learning an accurate architecture performance predictor. The accuracies of our searched neural architectures on NAS-Bench-101 and NAS-Bench-201 datasets are higher than that of the state-of-the-art methods and show the priority of the proposed method.