Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Evolving Normalization-Activation Layers
Authors: Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that Evo Norms work well on image classification models including Res Nets, Mobile Nets and Efficient Nets but also transfer well to Mask R-CNN with FPN/Spine Net for instance segmentation and to Big GAN for image synthesis, outperforming Batch Norm and Group Norm based layers in many cases. |
| Researcher Affiliation | Industry | Google Research, Brain Team Deep Mind EMAIL |
| Pseudocode | Yes | See pseudocode in Appendix A for details. |
| Open Source Code | Yes | Code for Evo Norms on Res Nets: https://github.com/tensorflow/tpu/tree/master/models/official/resnet |
| Open Datasets | Yes | We include experimental details in Appendix C, including those for the proxy task, search, reranking, and full-fledged evaluations. In summary, we did the search on CIFAR-10, and re-ranked the top-10 layers on a held-out set of Image Net to obtain the best 3 layers. |
| Dataset Splits | Yes | We discard layers that achieve less than 20%2 CIFAR-10 validation accuracy after training for 100 steps. |
| Hardware Specification | No | Evolution on CIFAR-10 took 2 days to complete with 5000 CPU workers. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We include experimental details in Appendix C, including those for the proxy task, search, reranking, and full-fledged evaluations. In summary, we did the search on CIFAR-10, and re-ranked the top-10 layers on a held-out set of Image Net to obtain the best 3 layers. ... Hyperparameters are inherited from the original implementations (usually in favor of BNs) without tuning w.r.t Evo Norms. |