Summary of Deep Dependency Networks and Advanced Inference Schemes For Multi-label Classification, by Shivvrat Arya et al.
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
by Shivvrat Arya, Yu Xiang, Vibhav Gogate
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 presents a unified framework called deep dependency networks (DDNs), which combines dependency networks and deep learning architectures for multi-label classification, focusing on image and video data. The framework’s primary advantage is ease of training compared to other probabilistic graphical models like Markov networks. When combined with deep learning architectures, DDNs provide an intuitive loss function for multi-label classification. However, they lack advanced inference schemes, requiring the use of Gibbs sampling. To address this challenge, novel inference schemes are proposed based on local search and integer linear programming. The methods are evaluated on six datasets (three video and three image) and compared to basic neural architectures and neural architectures combined with Markov networks. The results demonstrate the superiority of DDNs over the competing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new tool called deep dependency networks (DDNs) that helps computers recognize many things in pictures and videos at once. It’s easier to use than other similar tools, but it can’t do some advanced math problems as well. To fix this, the authors came up with new ways for DDNs to figure out what’s going on. They tested their tool on six different datasets and showed that it works better than two other popular approaches. |
Keywords
» Artificial intelligence » Classification » Deep learning » Inference » Loss function
