Summary of From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers, by Swaminathan Gurumurthy et al.
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
by Swaminathan Gurumurthy, Karnik Ram, Bingqing Chen, Zachary Manchester, Zico Kolter
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method addresses the challenges of training pose estimation and tracking models for robotics applications, which typically involve decomposing the problem into correspondence estimation and weighted least squares optimization. By iteratively refining these two problems, recent work has achieved state-of-the-art (SOTA) results across domains. However, training these models requires various tricks to stabilize and speed up the process. The paper identifies three plausible causes of noisy and higher variance gradients: flow loss interference, linearization errors in the bundle adjustment layer, and dependence of weight gradients on the residual. To mitigate these issues, the authors propose a simple yet effective solution that uses predicted weights to weight the correspondence objective in the training problem. This approach reduces the gradient variance and allows for faster training without sacrificing performance. The resulting method shows 2-2.5 times training speedups over a baseline visual odometry model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how machines can learn to predict poses (positions) in robotics tasks, like tracking objects. It’s hard to train these models because they need lots of data and processing power. The researchers found that the problem is caused by some issues with the way they’re training the models. They propose a new method to fix this problem, which helps make the training process faster and more stable. This can help machines do better in tasks like tracking objects or recognizing poses. |
Keywords
» Artificial intelligence » Optimization » Pose estimation » Tracking
