@conference {5547, title = { Learning to Learn: How to Continuously Teach Humans and Machines }, booktitle = { International Conference on Computer Vision (ICCV), 2023}, year = {2023}, month = {10/2023}, address = {Paris, France}, abstract = {

Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills. Designing a curriculum for teaching either a human or a machine shares the underlying goal of maximizing knowledge transfer from earlier to later tasks, while also minimizing forgetting of learned tasks. Prior research on curriculum design for image classification focuses on the ordering of training examples during a single offline task. Here, we investigate the effect of the order in which multiple distinct tasks are learned in a sequence. We focus on the online class-incremental continual learning setting, where algorithms or humans must learn image classes one at a time during a single pass through a dataset. We find that curriculum consistently influences learning outcomes for humans and for multiple continual machine learning algorithms across several benchmark datasets. We introduce a novel-object recognition dataset for human curriculum learning experiments and observe that curricula that are effective for humans are highly correlated with those that are effective for machines. As an initial step towards automated curriculum design for online class-incremental learning, we propose a novel algorithm, dubbed Curriculum Designer (CD), that designs and ranks curricula based on inter-class feature similarities. We find significant overlap between curricula that are empirically highly effective and those that are highly ranked by our CD. Our study establishes a framework for further research on teaching humans and machines to learn continuously using optimized curricula.

}, url = {https://openaccess.thecvf.com/content/ICCV2023/html/Singh_Learning_to_Learn_How_to_Continuously_Teach_Humans_and_Machines_ICCV_2023_paper.html}, author = {Parantak Singh and You Li and Ankur Sikarwar and Stan Weixian Lei and Difei Gao and Morgan B. Talbot and Ying Sun and Mike Zheng Shou and Gabriel Kreiman and Mengmi Zhang} } @article {5044, title = {Hypothesis-driven Online Video Stream Learning with Augmented Memory}, journal = {arXiv}, year = {2021}, month = {04/2021}, abstract = {

The ability to continuously acquire new knowledge without forgetting previous tasks remains a challenging problem for computer vision systems. Standard continual learning benchmarks focus on learning from static iid images in an offline setting. Here, we examine a more challenging and realistic online continual learning problem called online stream learning. Like humans, some AI agents have to learn incrementally from a continuous temporal stream of non-repeating data. We propose a novel model, Hypotheses-driven Augmented Memory Network (HAMN), which efficiently consolidates previous knowledge using an augmented memory matrix of "hypotheses" and replays reconstructed image features to avoid catastrophic forgetting. Compared with pixel-level and generative replay approaches, the advantages of HAMN are two-fold. First, hypothesis-based knowledge consolidation avoids redundant information in the image pixel space and makes memory usage far more efficient. Second, hypotheses in the augmented memory can be re-used for learning new tasks, improving generalization and transfer learning ability. Given a lack of online incremental class learning datasets on video streams, we introduce and adapt two additional video datasets, Toybox and iLab, for online stream learning. We also evaluate our method on the CORe50 and online CIFAR100 datasets. Our method performs significantly better than all state-of-the-art methods, while offering much more efficient memory usage. All source code and data are publicly available at this URL

}, doi = {10.48550/arXiv.2104.02206}, url = {https://arxiv.org/abs/2104.02206}, author = {Mengmi Zhang and Rohil Badkundri and Morgan B. Talbot and Rushikesh Zawar and Gabriel Kreiman} }