RepLKNet

• impressively manages to scale the kernel size to 31×31 with improved performance
• the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of ad- vanced ViTs such as Swin Transformer
• explore the possibility of training extreme convolutions larger than 31×31 and test whether the performance gap can be eliminated by strategically enlarging convolutions.