updated paper link

deepinsight · Mar 30, 2022 · 9ff1f80 · 9ff1f80
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diff --git a/recognition/arcface_torch/README.md b/recognition/arcface_torch/README.md
@@ -164,12 +164,4 @@ More details see
   booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
   year={2022}
 }
-@inproceedings{an2020partical_fc,
-  title={Partial FC: Training 10 Million Identities on a Single Machine},
-  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
-  Zhang, Debing and Fu Ying},
-  booktitle={Proceedings of International Conference on Computer Vision Workshop},
-  pages={1445-1449},
-  year={2020}
-}
 ```
diff --git a/recognition/arcface_torch/partial_fc.py b/recognition/arcface_torch/partial_fc.py
@@ -8,7 +8,7 @@
 
 class PartialFC(torch.nn.Module):
     """
-    https://arxiv.org/abs/2010.05222
+    https://arxiv.org/abs/2203.15565
     A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).
 
     When sample rate less than 1, in each iteration, positive class centers and a random subset of

diff --git a/recognition/partial_fc/README.md b/recognition/partial_fc/README.md
@@ -5,7 +5,7 @@ Partial FC is a distributed deep learning training framework for face recognitio
 
 
 ## Contents
-[Partial FC](https://arxiv.org/abs/2010.05222)
+[Partial FC](https://arxiv.org/abs/2203.15565)
 - [Largest Face Recognition Dataset: **Glint360k**](#Glint360K)
 - [Docker](#Docker)
 - [Performance On Million Identities](#Benchmark)
@@ -144,15 +144,15 @@ The torrent has been released.
 ## Citation
 If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper: 
 
-[Partial FC](https://arxiv.org/abs/2010.05222)
+[Partial FC](https://arxiv.org/abs/2203.15565)
 ```
-@inproceedings{an2020partical_fc,
-  title={Partial FC: Training 10 Million Identities on a Single Machine},
-  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
-  Zhang, Debing and Fu Ying},
-  booktitle={Arxiv 2010.05222},
-  year={2020}
+@inproceedings{an2022pfc,
+  title={Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
+  author={An, Xiang and Deng, Jiangkang and Guo, Jia and Feng, Ziyong and Zhu, Xuhan and Jing, Yang and Tongliang, Liu},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2022}
 }
+
 ```
 
 

diff --git a/recognition/partial_fc/mxnet/README_CN.md b/recognition/partial_fc/mxnet/README_CN.md
@@ -1,6 +1,6 @@
 ## 目录
 ## Contents
-[Partial FC](https://arxiv.org/abs/2010.05222)
+[Partial FC](https://arxiv.org/abs/2203.15565)
 - [如何安装](#如何安装)
 - [如何运行](#如何运行)
 - [错误排查](#错误排查)

diff --git a/recognition/partial_fc/mxnet/memory_module.py b/recognition/partial_fc/mxnet/memory_module.py
@@ -19,7 +19,7 @@ class SampleDistributeModule(object):
     RTX2080Ti can complete classification tasks with 100 million of identities.
 
     See the original paper:
-    https://arxiv.org/abs/2010.05222
+    https://arxiv.org/abs/2203.15565
 
     Parameters
     ----------

diff --git a/recognition/partial_fc/mxnet/train_memory.py b/recognition/partial_fc/mxnet/train_memory.py
@@ -1,10 +1,3 @@
-"""
-Author: {Xiang An, XuHan Zhu, Yang Xiao} in DeepGlint,
-Partial FC: Training 10 Million Identities on a Single Machine
-See the original paper:
-https://arxiv.org/abs/2010.05222
-"""
-
 import argparse
 import logging
 import os