Yinuo Peng

Our recent project compares human continual learning with deep neural networks in classification tasks. One of the main challenges in machine learning is that the network readily forgets previously learned information as it encounters new information. For example, a deep network pre-trained with some images will acquire useful features for tasks such as classification. However, when training the network with more images from other sources to further improve its performance or to perform new tasks, it will show complete forgetting of the previously learned tasks, which is called catastrophic forgetting. In collaboration with Prof. Derek Hoiem and Zhen Zhu at the Department of Computer Science, we are examining human category learning under the same scenario to see whether and why the learning sequence affects humans and neural networks differently.