讀文獻 Large Scale Learning of General Visual Representations for Transfer 閱讀心得
摘要 這篇文章擴大了預訓練的規模，並提出一種簡單易用的遷移學習算法叫做大遷移（BiT）。通過組合一系列仔細選擇的組件，遷移時應用簡單的啓發式算法，在20個數據集上實現了非常好的識別效果。重點是在小數據集上，86.4%在ILSVRC-2012，這個數據集每個類只有25張圖片；97.6%在CIFAR10（修改成每個類只有10張圖片的形式）。 Introduction 要執行遷移學習，首先要在大型通用數
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相關文章
- 1. 論文閱讀筆記《Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy》
- 2. 論文閱讀：Large-Scale Visual Relationship Understanding
- 3. 文獻閱讀 - Deep Contextualized Word Representations
- 4. 文獻閱讀 - Deep Residual Learning for Image Recognition
- 5. 文獻閱讀筆記-ALBERT ： A lite BERT for self-supervised learning of language representations
- 6. 論文閱讀：DeepWalk Online Learning of Social Representations
- 7. 文獻閱讀（九）KDD2014-DeepWalk：online learning of social representations
- 8. 論文寫作解讀：A Simple Framework for Contrastive Learning of Visual Representations
- 9. 文獻閱讀報告-Learning Lane Graph Representations for Motion Forecasting
- 10. Discriminative Transfer Learning for General Image Restoration