Papers › Learning to Remember Rare Events
Learning to Remember Rare Events
Łukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio
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. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. Our 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 remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 20-way | ConvNet with Memory Module | Accuracy | 95% | #14 of 20 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 5-way | ConvNet with Memory Module | Accuracy | 98.4 | #13 of 17 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 20-way | ConvNet with Memory Module | Accuracy | 98.6% | #11 of 19 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 5-way | ConvNet with Memory Module | Accuracy | 99.6 | #10 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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