Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization 在部分/全部隨機標籤的CIFAR-10數據集，以及加入了高斯噪音的圖片上的表現。如果在隨機生成的分類標籤上，模型表現的也很好，這對於模型的泛化能力意味着什麼? 先讓我們看看文中給出的數據，A圖指出，不管怎樣在模型中怎樣引入隨機性，在圖像中加入隨機噪音，對像素進行隨機洗牌
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相關文章
- 1. 『 論文閱讀』Understanding deep learning requires rethinking generalization
- 2. 論文筆記 understanding deep learning requires rethinking generalization
- 3. What’s New in Deep Learning Research: Understanding Meta-Learning
- 4. (轉) Awesome - Most Cited Deep Learning Papers
- 5. Applied Deep Learning Resources
- 6. 【Deep Learning】筆記：Understanding the difficulty of training deep feedforward neural networks
- 7. LEARNING NOTES FROM DEEP LEARNING
- 8. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化
- 9. Machine Learning & Deep Learning 論文閱讀筆記
- 10. ON LARGE BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA