Summary of Deep Companion Learning: Enhancing Generalization Through Historical Consistency, by Ruizhao Zhu et al.
Deep Companion Learning: Enhancing Generalization Through Historical Consistency
by Ruizhao Zhu, Venkatesh Saligrama
First submitted to arxiv on: 26 Jul 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 We propose Deep Companion Learning (DCL), a novel training method for Deep Neural Networks (DNNs) that enhances generalization by penalizing inconsistent model predictions compared to its historical performance. DCL trains a deep-companion model (DCM) using previous versions of the primary model, providing forecasts on new inputs. This companion model uncovers a latent semantic structure within the data, offering targeted supervision for the primary model to address challenging scenarios. Our approach is validated through theoretical analysis and extensive experimentation on benchmark datasets like CIFAR-100, Tiny-ImageNet, and ImageNet-1K using various architectural models such as ShuffleNetV2, ResNet, and Vision Transformer, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to train artificial intelligence (AI) models called Deep Companion Learning. This method helps AI models make better predictions by looking at how they did in the past. We use an old version of the model to predict what it would say about new information, and this helps the model learn from its mistakes. Our approach works really well on lots of different datasets, including pictures, using various types of AI models. This means our AI models can get better at making predictions and learning from their past experiences. |
Keywords
* Artificial intelligence * Generalization * Resnet * Vision transformer
