Update README.md

README.md

@@ -139,16 +139,16 @@ My open-sourced papers and repos:
 
 The **Structural Re-parameterization Universe**:
 
-1. (preprint, 2021) **A powerful MLP-style CNN building block**\
-[RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition](https://arxiv.org/abs/2105.01883)\
+1. RepMLP (preprint, 2021) **MLP-style building block and Architecture**\
+[RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality](https://arxiv.org/abs/2112.11081)\
 [code](https://github.com/DingXiaoH/RepMLP).
 
-2. (CVPR 2021) **A super simple and powerful VGG-style ConvNet architecture**. Up to **83.55%** ImageNet top-1 accuracy!\
+2. RepVGG (CVPR 2021) **A super simple and powerful VGG-style ConvNet architecture**. Up to **84.16%** ImageNet top-1 accuracy!\
 [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)\
 [code](https://github.com/DingXiaoH/RepVGG).
 
-3. (preprint, 2020) **State-of-the-art** channel pruning\
-[Lossless CNN Channel Pruning via Decoupling Remembering and Forgetting](https://arxiv.org/abs/2007.03260)\
+3. ResRep (ICCV 2021) **State-of-the-art** channel pruning (Res50, 55\% FLOPs reduction, 76.15\% acc)\
+[ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting](https://openaccess.thecvf.com/content/ICCV2021/papers/Ding_ResRep_Lossless_CNN_Pruning_via_Decoupling_Remembering_and_Forgetting_ICCV_2021_paper.pdf)\
 [code](https://github.com/DingXiaoH/ResRep).
 
 4. ACB (ICCV 2019) is a CNN component without any inference-time costs. The first work of our Structural Re-parameterization Universe.\
@@ -159,6 +159,10 @@ The **Structural Re-parameterization Universe**:
 [Diverse Branch Block: Building a Convolution as an Inception-like Unit](https://arxiv.org/abs/2103.13425)\
 [code](https://github.com/DingXiaoH/DiverseBranchBlock).
 
+6. COMING SOON
+
+7. COMING SOON
+
 **Model compression and acceleration**:
 
 1. (CVPR 2019) Channel pruning: [Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure](http://openaccess.thecvf.com/content_CVPR_2019/html/Ding_Centripetal_SGD_for_Pruning_Very_Deep_Convolutional_Networks_With_Complicated_CVPR_2019_paper.html)\