change fale

README.md

@@ -6,20 +6,35 @@ This repository contains the code for DenseNet introduced in the following paper
 
 [Gao Huang](http://www.cs.cornell.edu/~gaohuang/)\*, [Zhuang Liu](https://liuzhuang13.github.io/)\*, [Laurens van der Maaten](https://lvdmaaten.github.io/) and [Kilian Weinberger](https://www.cs.cornell.edu/~kilian/) (\* Authors contributed equally).
 
+and its journal version
 
-**Now with much more memory efficient implementation!** Please check the [technical report](https://arxiv.org/pdf/1707.06990.pdf) and [code](https://github.com/liuzhuang13/DenseNet/tree/master/models) for more infomation.
+[Convolutional Networks with Dense Connectivity](http://www.gaohuang.net/papers/DenseNet_Journal.pdf) (TPAMI 2019) 
+
+[Gao Huang](http://www.cs.cornell.edu/~gaohuang/), [Zhuang Liu](https://liuzhuang13.github.io/), [Geoff Pleiss](https://geoffpleiss.com/), [Laurens van der Maaten](https://lvdmaaten.github.io/) and [Kilian Weinberger](https://www.cs.cornell.edu/~kilian/).
+
+
+**Now with memory-efficient implementation!** Please check the [technical report](https://arxiv.org/pdf/1707.06990.pdf) and [code](https://github.com/liuzhuang13/DenseNet/tree/master/models) for more infomation.
 
 The code is built on [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch).
 
 ### Citation
 If you find DenseNet useful in your research, please consider citing:
 
+	@article{huang2019convolutional,
+	 title={Convolutional Networks with Dense Connectivity},
+	 author={Huang, Gao and Liu, Zhuang and Pleiss, Geoff and Van Der Maaten, Laurens and Weinberger, Kilian},
+	 journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+	 year={2019}
+	 }
+
 	@inproceedings{huang2017densely,
-	  title={Densely connected convolutional networks},
+	  title={Densely Connected Convolutional Networks},
 	  author={Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q },
 	  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 	  year={2017}
 	}
+
+
 
 
 ## Other Implementations
@@ -114,6 +129,8 @@ DenseNet-BC (L=190, k=40)|25.6M |- |**3.46** | -|**17.18**
 ## Results on ImageNet and Pretrained Models
 ### Torch
 
+Note: the pre-trained models in Torch are deprecated and no longer maintained. Please use PyTorch's pre-trained [DenseNet models](https://pytorch.org/vision/stable/models.html) instead.
+
 #### Models in the original paper
 The Torch models are trained under the same setting as in [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). The error rates shown are 224x224 1-crop test errors.
 
@@ -130,10 +147,10 @@ More accurate models trained with the memory efficient implementation in the [te
 
 | Network       |  Top-1 error | Torch Model |
 | ------------- | ----------- | ------------ |
-| DenseNet-264 (k=32)  |  22.1 |    [Download (256MB)](https://drive.google.com/file/d/0By1NwtA2JPGzdVRqOEotMUZrbTA/view?usp=sharing)
-| DenseNet-232 (k=48)  | 21.2 | [Download (426MB)](https://drive.google.com/open?id=0By1NwtA2JPGzdkRDaWQ5M3VHTDg)
-| DenseNet-cosine-264 (k=32) | 21.6  |  [Download (256MB)](https://drive.google.com/file/d/0By1NwtA2JPGzRDhxWGo2a3pOTjA/view?usp=sharing)
-| DenseNet-cosine-264 (k=48) | 20.4   | [Download (557MB)](https://drive.google.com/file/d/0By1NwtA2JPGzcnFDSE1HQVh4c0k/view?usp=sharing)
+| DenseNet-264 (k=32)  |  22.1 |    [Download (256MB)](https://drive.google.com/file/d/0By1NwtA2JPGzdVRqOEotMUZrbTA/view?usp=sharing&resourcekey=0-5D_u52k6wCy6doaLwdBfqw)
+| DenseNet-232 (k=48)  | 21.2 | [Download (426MB)](https://drive.google.com/file/d/0By1NwtA2JPGzdkRDaWQ5M3VHTDg/view?usp=sharing&resourcekey=0-yl4XWJ2J6GZaF6RPj43QHQ)
+| DenseNet-cosine-264 (k=32) | 21.6  |  [Download (256MB)](https://drive.google.com/file/d/0By1NwtA2JPGzRDhxWGo2a3pOTjA/view?usp=sharing&resourcekey=0-AOIBvppNz9cdDcuKvH7_aQ)
+| DenseNet-cosine-264 (k=48) | 20.4   | [Download (557MB)](https://drive.google.com/file/d/0By1NwtA2JPGzcnFDSE1HQVh4c0k/view?usp=sharing&resourcekey=0-uAyd9bsTas2twCzAZ1DUpA)
 
 
 ### Caffe
@@ -173,11 +190,15 @@ Thus, for practical use, we suggest picking one model from those Wide-DenseNet-B
 
 
 ## Updates
-**08/23/2017:**
+**12/10/2019:**
+
+1. Journal version (accepted by IEEE TPAMI) released.
+
+08/23/2017:
 
 1. Add supporting code, so one can simply *git clone* and run.
 
-**06/06/2017:**
+06/06/2017:
 
 1. Support **ultra memory efficient** training of DenseNet with *customized densely connected layer*.
 
@@ -205,6 +226,6 @@ Thus, for practical use, we suggest picking one model from those Wide-DenseNet-B
 
 ## Contact
 liuzhuangthu at gmail.com  
-gh349 at cornell.edu   
+gaohuang at tsinghua.edu.cn   
 Any discussions, suggestions and questions are welcome!
 

README777.MD

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