Update README.md

README.md

@@ -204,5 +204,4 @@ If you plan to contribute new features, utility functions or extensions to the c
 
 * **[Deep Symmetric Network for Underexposed Image Enhancement with Recurrent Attentional Learning](https://www.shaopinglu.net/publications_files/ICCV21_Image_Enhancement.pdf)**
 * The results in Fig 1. for DeepLPF in this paper are incorrect. An example of inference for one of the images in Fig 1. is supplied [here](https://github.com/sjmoran/DeepLPF/blob/ecdc6f08cc96ff727a8246874bce06726949068e/images/004668_TEST_25_354_PSNR_21.848_SSIM_0.858.jpg). For replication, we provide our DeepLPF pre-trained model for their dataset [here](https://github.com/sjmoran/DeepLPF/blob/7b147dd819b2e4c8e9898c411f23887250cb9afe/pretrained_models/adobe_distort_and_recover/deeplpf_validpsnr_23.629675866286313_validloss_0.030986817553639412_testpsnr_23.629675866286313_testloss_0.030986817553639412_epoch_49_model.pt). The quantitative results in Table 1 for DeepLPF should be: **23.63dB, 0.875 SSIM**. 
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-29th September 2021: The paper authors have graciously noted this issue and have issued an errata to their paper [here](https://www.shaopinglu.net/proj-iccv21/ImageEnhancement.html). We thank the authors for their time commitment and collaboration in re-checking the DeepLPF result in their paper.
+* **29th September 2021**: The paper authors have graciously noted this issue and have issued an errata to their paper [here](https://www.shaopinglu.net/proj-iccv21/ImageEnhancement.html). We thank the authors for their time commitment and collaboration in re-checking the DeepLPF result in their paper.