The great success of Deep Neural Networks (DNNs) has inspired the algorithmic development of DNN-based Fixed-Point (DNN-FP) for computer vision tasks. DNN-FP methods, trained by Back-Propagation Through Time or computing the inaccurate inversion of the Jacobian, suffer from inferior representation ability. Motivated by the representation power of the Transformer, we propose a framework to unroll the FP and approximate each unrolled process via Transformer blocks, called FPformer. To reduce the high consumption of memory and computation, we come up with FPRformer by sharing parameters between the successive blocks. We further design a module to adapt Anderson acceleration to FPRformer to enlarge the unrolled iterations and improve the performance, called FPAformer. In order to fully exploit the capability of the Transformer, we apply the proposed model to image restoration, using self-supervised pre-training and supervised fine-tuning. 161 tasks from 4 categories of image restoration problems are used in the pre-training phase. Hereafter, the pre-trained FPformer, FPRformer, and FPAformer are further fine-tuned for the comparison scenarios. Using self-supervised pre-training and supervised fine-tuning, the proposed FPformer, FPRformer, and FPAformer achieve competitive performance with state-ofthe-art image restoration methods and better training efficiency. FPAformer employs only 29.82% parameters used in SwinIR models, and provides superior performance after fine-tuning. To train these comparison models, it takes only 26.9% time used for training SwinIR models. It provides a promising way to introduce the Transformer in low-level vision tasks.

In off-policy reinforcement learning, prioritized experience replay plays an important role. However, the centralized prioritized experience replay becomes the bottleneck for efficient training. We propose to approximate the centralized prioritized experience replay in a distributed and decentralized way under certain mild assumptions. To be specific, each actor stores samples in its local replay in the same way as prioritized experience replay, the learner fetches a batch of samples from these replays following a certain strategy. We implement a Deep Q-Learning off-policy algorithm upon the proposed framework. The comparison experiments are performed on a commonly used subset of the Atari-57 learning environment. The experimental results show that the proposed framework speeds up training as the number of actors increases. With the same algorithm and hyper-parameter settings, the proposed framework with 16 actors achieves superior performance that Ape-X with 32 and even more actors does.

No abstract