Summary of Comparing Supervised Learning Dynamics: Deep Neural Networks Match Human Data Efficiency but Show a Generalisation Lag, by Lukas S. Huber et al.
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lag
by Lukas S. Huber, Fred W. Mast, Felix A. Wichmann
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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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 The paper investigates the learning dynamics between human observers and deep neural networks (DNNs) in image classification. Unlike previous studies, which focused on comparing the end-results of learning processes, this study examines how representations emerge during the acquisition process. The authors develop a constrained supervised learning environment to align conditions for both humans and DNNs. Across the entire learning process, they evaluate and compare how well learned representations can be generalized to previously unseen test data. The results show that DNNs demonstrate comparable data efficiency to human learners, challenging prevailing assumptions in the field. However, the study also reveals representational differences between humans and DNNs, with DNNs exhibiting a pronounced generalization lag. The findings have implications for understanding how machines learn and generalize information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares how humans and computers (deep neural networks) learn to recognize images. Unlike previous studies, this one looks at what happens during the learning process, not just the final results. The researchers create a special environment where both humans and computers are given similar tasks and feedback. They find that computers can learn to recognize images quickly and efficiently, but they take longer to generalize their knowledge to new images. Humans, on the other hand, seem to learn to recognize images immediately without needing to practice first. This study helps us understand how machines learn and what makes them different from humans. |
Keywords
* Artificial intelligence * Generalization * Image classification * Supervised
