Survey on crop-paste argumentation

3D Geomerty estimation

• Synthetic Data for Text Localisation in Natural Images

Indoor

• Synthesizing Training Data for Object Detection in Indoor Scenes

Crop-paste for instance detection

• Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection(Randomly crop paste)
• InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting

Crop-paste for object detection considering context

• Modeling Visual Context is Key to Augmenting Object Detection Datasets
• Context-Aware Synthesis and Placement of Object Instances
• Data Augmentation for Object Detection via Progressive and Selective Instance-Switching

Survey on dataset distilation

• Dataset Distilation

• Soft-Label Dataset Distillation and Text Dataset Distillation(Waterloo 2019)

    Apply probability distribution instead hard lable 
  

Survey on coreset selection

Core-set method for classical machine learning

For SVM

• Core Vector Machines:Fast SVM Training on Very Large Data Sets

For k-Means

• Smaller Coresets for k-Median and k-Means Clustering

Data summarization based on submodular maximization

• Using document summarization techniques for speech data subset selection
• Submodular subset selection for large-scale speech training data
• Learning mixtures of submodular functions for image collection summarization
• Unsupervised data selection and word-morph mixed language model for tamil low-resource keyword search

Core-set method for Nerual Network

Example Forgetting

• An empirical study of example forgetting during deep neural network learning

    Examples not forgot may be the "core examples"
  

Learning with Proxy

• Selection via Proxy: Efficient Data Selection for Deep Learning (Stanford 2019)

Survey on Active Learning

Active Learning for classical method

• Burr Settles. Active learning

Active Learning for Deep Learning

Deep Bayesian Active Learning

• Dropout as a bayesian approximation: Representing model uncertainty in deep learning

    Relation between deep learning and bayesian approximation
  

• Deep bayesian active learning with image data

    Apply active learning in deep bayesian learning 
  

• BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Active learning for CNN

• Active learning for convolutional neural networks: A core-set approach. (Stanford 2018)

    Also introduce a core-set selection method for CNN