论文标题
通过变异自动编码器在视频序列中持续学习预测模型
Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
论文作者
论文摘要
本文提出了一种对预测模型进行持续学习的方法，以促进视频序列中未来帧的推断。为了获得第一个给定的体验，最初的变性自动编码器以及一组完全连接的神经网络被用于分别学习视频帧的外观及其在潜在空间级别上的动态。通过采用改编的马尔可夫跳跃粒子滤波器，该提出的方法识别新情况并将其集成为预测模型，以避免灾难性忘记以前学习的任务。为了评估所提出的方法，本文使用了在受控环境中执行不同任务的车辆的视频序列。
This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial Variational Autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment.
