Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones

By Francesco Pelosin. 2022

16 citations recorded by the source corpus.

In this short paper, we propose a baseline (off-the-shelf) for Continual Learning of Computer Vision problems, by leveraging the power of pretrained models. By doing so, we devise a simple approach achieving strong performance for most of the common benchmarks. Our approach is fast since requires no parameters updates and has minimal memory requirements (order of KBytes). In particular, the"training"phase reorders data and exploit the power of pretrained models to compute a class prototype and fill a memory bank. At inference time we match the closest prototype through a knn-like approach, providing us the prediction. We will see how this naive solution can act as an off-the-shelf continual learning system. In order to better consolidate our results, we compare the devised pipeline with common CNN models and show the superiority of Vision Transformers, suggesting that such architectures have the ability to produce features of higher quality. Moreover, this simple pipeline, raises the same questions raised by previous works \cite{gdumb} on the effective progresses made by the CL community especially in the dataset considered and the usage of pretrained models. Code is live at https://github.com/francesco-p/off-the-shelf-cl

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Hang Su
  2. Jun Zhu
  3. Liyuan Wang
  4. Xingxing Zhang
  5. Tianyu Pang
  6. Yinpeng Dong
  7. Vamsi Krishna Ithapu
  8. Mirella Lapata

In Computer Vision and Pattern Recognition

  1. Hang Su
  2. Jun Zhu
  3. Liyuan Wang
  4. Xingxing Zhang
  5. Yinpeng Dong
  6. Vamsi Krishna Ithapu
  7. Alan L. Yuille
  8. Tianyu Pang

In Meta & Continual Learning

  1. Xingxing Zhang
  2. Liyuan Wang
  3. Hang Su
  4. Jun Zhu
  5. Simone Scardapane
  6. Valerio Marsocci
  7. Liyuan Wang
  8. Hongwei Yan

Citations in the corpus