# Reconsidering CO2 emissions from Computer Vision

arXiv: https://arxiv.org/abs/2104.08702 · HTML: https://mlsanity.com/paper/2104.08702/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 61 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [SNAS: Stochastic Neural Architecture Search](https://mlsanity.com/paper/1812.09926/) (2018)
- [Searching for A Robust Neural Architecture in Four GPU Hours](https://mlsanity.com/paper/1910.04465/) (2019)
- [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://mlsanity.com/paper/1708.05344/) (2017)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models](https://mlsanity.com/paper/2003.11142/) (2020)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://mlsanity.com/paper/2004.05565/) (2020)
- [Graph HyperNetworks for Neural Architecture Search](https://mlsanity.com/paper/1810.05749/) (2018)
- [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search](https://mlsanity.com/paper/1911.12126/) (2019)
- [Blockwisely Supervised Neural Architecture Search with Knowledge Distillation](https://mlsanity.com/paper/1911.13053/) (2019)
- [Network Pruning via Transformable Architecture Search](https://mlsanity.com/paper/1905.09717/) (2019)
- [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://mlsanity.com/paper/1910.05733/) (2019)
- [SGAS: Sequential Greedy Architecture Search](https://mlsanity.com/paper/1912.00195/) (2019)
- [AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling](https://mlsanity.com/paper/2011.09011/) (2020)
- [Neural Architecture Transfer](https://mlsanity.com/paper/2005.05859/) (2020)
- [MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation](https://mlsanity.com/paper/2003.12238/) (2020)
- [Densely Connected Search Space for More Flexible Neural Architecture Search](https://mlsanity.com/paper/1906.09607/) (2019)
- [Path-Level Network Transformation for Efficient Architecture Search](https://mlsanity.com/paper/1806.02639/) (2018)
- [AtomNAS: Fine-Grained End-to-End Neural Architecture Search](https://mlsanity.com/paper/1912.09640/) (2019)
- [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://mlsanity.com/paper/2010.15821/) (2020)

## Built on by (most cited first, 9 of 9)

- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [End-to-End Supervised Multilabel Contrastive Learning](https://mlsanity.com/paper/2307.03967/) (2023)
- [Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms](https://mlsanity.com/paper/2510.07317/) (2025)
- [Exploiting Explainable Metrics for Augmented SGD](https://mlsanity.com/paper/2203.16723/) (2022)
- [Estimating Environmental Cost Throughout Model's Adaptive Life Cycle](https://mlsanity.com/paper/2408.01446/) (2024)
- [P4AI: Approaching AI Ethics through Principlism](https://mlsanity.com/paper/2111.14062/) (2021)
- [CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging](https://mlsanity.com/paper/2506.05360/) (2025)
- [NoFADE: Analyzing Diminishing Returns on CO2 Investment](https://mlsanity.com/paper/2111.14059/) (2021)

## Abstract (arXiv)

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the Computer Vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.