3 accepted papers
Conventionally, deep neural networks are trained offline, relying on a large dataset prepared in advance. This paradigm is often challenged in real-world applications, e.g. online services that involve continuous streams of incoming data. Recently, incremental learning receives increasing attention,…
Lifelong learning aims at adapting a learned model to new tasks while retaining the knowledge gained earlier. A key challenge for lifelong learning is how to strike a balance between the preservation on old tasks and the adaptation to a new one within a given model. Approaches that combine both obje…
In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, i…