In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network(FRCNN) to solvethe separation task. This model containsbottom-up,top-downandlateral connections tofuse information processed atvarious time-scales represented by stages.

Text-to-image diffusion has attracted vast attention due to its impressive image-generation capabilities.

We provide our source codes to ensure the reproducibility of our experimental results. Below we summarize several critical aspects w.r .tthe The datasets we used are all publicly accessible, which is introduced in Appendix E.1. For long-tailed subsets, we strictly follows previous work [29] on CIFAR-100-L T to avoid the bias attribute to the sampling randomness. On ImageNet-L T and Places-L T, we employ the widely-used data split first introduced in [44]. All the experiments are conducted on NVIDIA GeForce RTX 3090 with Python 3.7 and Pytorch 1.7.

Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood.