# PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

arXiv: https://arxiv.org/abs/2004.13513 · HTML: https://mlsanity.com/paper/2004.13513/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Representations for New Sound Classes With Continual Self-Supervised Learning](https://mlsanity.com/paper/2205.07390/) (2022)
- [MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning](https://mlsanity.com/paper/2605.19080/) (2026)

## Abstract (arXiv)

Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning. In this work, we propose PODNet, a model inspired by representation learning. By carefully balancing the compromise between remembering the old classes and learning new ones, PODNet fights catastrophic forgetting, even over very long runs of small incremental tasks --a setting so far unexplored by current works. PODNet innovates on existing art with an efficient spatial-based distillation-loss applied throughout the model and a representation comprising multiple proxy vectors for each class. We validate those innovations thoroughly, comparing PODNet with three state-of-the-art models on three datasets: CIFAR100, ImageNet100, and ImageNet1000. Our results showcase a significant advantage of PODNet over existing art, with accuracy gains of 12.10, 6.51, and 2.85 percentage points, respectively. Code is available at https://github.com/arthurdouillard/incremental_learning.pytorch