Corpus ID: 12122362

Learning to Remember Rare Events

@article{Kaiser2017LearningTR,
  title={Learning to Remember Rare Events},
  author={Lukasz Kaiser and Ofir Nachum and Aurko Roy and S. Bengio},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.03129}
}
Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. [...] Key MethodOur memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to…Expand
Memory Matching Networks for One-Shot Image Recognition
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Labeled Memory Networks for Online Model Adaptation
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition
  • Linchao Zhu, Y. Yang
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
Memory-based Parameter Adaptation
Label Organized Memory Augmented Neural Network
Robust Compare Network for Few-Shot Learning
Self-Attention Relation Network for Few-Shot Learning
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 36 REFERENCES
Sequence to Sequence Learning with Neural Networks
Siamese Neural Networks for One-Shot Image Recognition
Long Short-Term Memory
Can Active Memory Replace Attention?
Hierarchical Memory Networks
ImageNet classification with deep convolutional neural networks
Order Matters: Sequence to sequence for sets
...
1
2
3
4
...