Learning to Navigate for Fine-grained Classification 论文学习
Learning to Navigate for Fine-grained Classification 论文地址 参考博客 介绍 问题描述：细粒度分类任务是用来区分上一级公共类的子类，例如野生鸟类、汽车型号。这些子类通常是由各个领域专家根据特定的规则定义的，这些规则通常关注特定区域的细微差别。 主要挑战：由于类间变化较小，找到具有判别能力的特征相对普通分类任务更加困难。 现有问题 强监督学习：利
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相关文章
- 1. 论文阅读笔记 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 2. Learning to Navigate for Fine-grained Classification
- 3. [小样本学习] 论文笔记 Learning to Self-Train for Semi-Supervised Few-Shot Classification
- 4. 论文阅读笔记《Learning to Select Base Classes for Few-shot Classification》
- 5. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 论文笔记
- 6. Adversarial Multi-task Learning for Text Classification
- 7. LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS 阅读笔记
- 8. Learning to Navigate in Cities Without a Map 理解
- 9. 深度学习论文阅读：Convolutional Neural Networks for Sentence Classification （TextCNN）
- 10. 【主动学习论文】Learning Loss for Active Learning, CVPR 2019