论文：Baby Steps Towards Few-Shot Learning with Multiple Semantics

时间 2021-01-04

标签语义分割机器学习神经网络繁體版

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1、Baby Steps Towards Few-Shot Learning with Multiple Semantics 目的：记录下对文章的理解摘要：额外的语义信息可以显著提高少样本学习的能力，论文在少样本学习中结合了多种丰富的语义，这里语义信息包括类别标签、属性和自然语言描述等信息。论文在miniImageNet 和CUB few-shot benchmark做了丰富实验。下面几个问题可

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1. 论文阅读笔记《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
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1. 论文阅读笔记《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
2. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之论文详解
3. 论文笔记：Towards perspective-free object counting with deep learning
4. 论文-《Towards Optimal Binary Code Learning via Ordinal Embedding》
5. [论文解读]Towards Improved Testing For Deep Learning
6. (MIL)Robust Object Tracking with Online Multiple Instance Learning论文学习笔记
7. [论文阅读]Learning Discriminative Features with Multiple Granularityfor Person Re-Identification
8. Visual Tracking with Online Multiple Instance Learning (MIL)目标跟踪论文笔记
9. 论文略读 | Learning End-to-End Goal-Oriented Dialog with Multiple Answers
10. Google's Machine Learning Crash Course #04# First Steps with TensorFlow

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