Summary of Uncle: Unsupervised Continual Learning Of Depth Completion, by Suchisrit Gangopadhyay et al.
UnCLe: Unsupervised Continual Learning of Depth Completion
by Suchisrit Gangopadhyay, Xien Chen, Michael Chu, Patrick Rim, Hyoungseob Park, Alex Wong
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes UnCLe, a standardized benchmark for unsupervised continual learning in multimodal depth estimation tasks, specifically focusing on depth completion. The task involves inferring dense depth maps from paired RGB images and sparse depth maps. The authors highlight the limitations of existing methods, which are typically trained on stationary datasets and “catastrophically forget” previously learned information when adapting to novel non-stationary distributions. UnCLe simulates these scenarios by training depth completion models on sequences of datasets with diverse scenes captured from different domains using various visual and range sensors. The authors translate representative continual learning methods for unsupervised learning and benchmark them for indoor and outdoor tasks, evaluating the degree of catastrophic forgetting through standard metrics. Additionally, they introduce model inversion quality as an alternative measure of forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special challenge for computers that can see and understand 3D environments. It’s like training a computer to recognize objects in different rooms or cities. The problem is that most computers forget what they learned when they move to a new place. This paper makes a “benchmark” – a set of rules – to help computers learn better and remember more. It uses special cameras and sensors to create realistic scenarios for the computers to practice. The goal is to make it easier for computers to understand 3D environments, which could be useful in many areas like self-driving cars or virtual reality. |
Keywords
» Artificial intelligence » Continual learning » Depth estimation » Unsupervised
