As there exist competitive subnetworks within a dense network in concert with Lottery Ticket Hypothesis, we introduce a novel neuron-wise task incremental learning method, namely Data-free Subnetworks (DSN), which attempts to enhance the elastic knowledge transfer across the tasks that sequentially arrive. Specifically, DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay.

As there exist competitive subnetworks within a dense network in concert with Lottery Ticket Hypothesis, we introduce a novel neuron-wise task incremental learning method, namely Data-free Subnetworks (DSN), which attempts to enhance the elastic knowledge transfer across the tasks that sequentially arrive. Specifically, DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay. The comprehensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed DSN in the context of task-incremental learning by comparing it to several state-of-the-art baselines. In particular, DSN enables the knowledge transfer to the earlier tasks, which is often overlooked by prior efforts.

This work presents an incremental learning approach for autonomous agents to learn new tasks in a non-stationary environment. Updating a DNN model-based agent to learn new target tasks requires us to store past training data and needs a large labeled target task dataset. Few-shot task incremental learning methods overcome the limitation of labeled target datasets by adapting trained models to learn private target classes using a few labeled representatives and a large unlabeled target dataset. However, the methods assume that the source and target tasks are stationary. We propose a one-shot task incremental learning approach that can adapt to non-stationary source and target tasks. Our approach minimizes adversarial discrepancy between the model's feature space and incoming incremental data to learn an updated hypothesis. We also use distillation loss to reduce catastrophic forgetting of previously learned tasks. Finally, we use Gaussian prototypes to generate exemplar instances eliminating the need to store past training data. Unlike current work in task incremental learning, our model can learn both source and target task updates incrementally. We evaluate our method on various problem settings for incremental object detection and disease prediction model update. We evaluate our approach by measuring the performance of shared class and target private class prediction. Our results show that our approach achieved improved performance compared to existing state-of-the-art task incremental learning methods.