重新思考FUN：频域利用网络
Rethinking FUN: Frequency-Domain Utilization Networks
摘要：
近年来，对高效神经网络架构的研究受到了广泛关注，现代架构不仅关注准确性，还关注推理时间和模型大小。在这里，我们介绍FUN，一个新颖的频域利用网络家族。这些网络利用频域的固有效率，直接在该域中工作，用离散余弦变换表示。使用现代技术和构建模块，如复合缩放和反向残差层，我们生成了一组这样的网络，允许在大小，延迟和准确性之间进行平衡，同时优于竞争的基于RGB的模型。广泛的评估验证了我们的网络提供了强大的替代方案，以前的方法。此外，我们表明，在频域中工作，允许在推理时间的输入动态压缩，而无需任何显式的变化的架构。
英文摘要：
The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and model size. Here, we present FUN, a family of novel Frequency-domain Utilization Networks. These networks utilize the inherent efficiency of the frequency-domain by working directly in that domain, represented with the Discrete Cosine Transform. Using modern techniques and building blocks such as compound-scaling and inverted-residual layers we generate a set of such networks allowing one to balance between size, latency and accuracy while outperforming competing RGB-based models. Extensive evaluations verifies that our networks present strong alternatives to previous approaches. Moreover, we show that working in frequency domain allows for dynamic compression of the input at inference time without any explicit change to the architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
