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haimengzhao / CAE-ADMM

CAE-ADMM: Implicit Bitrate Optimization via ADMM-Based Pruning in Compressive Autoencoders
https://arxiv.org/abs/1901.07196
MIT License
47 stars 9 forks source link

Reproduce the quality result #2

Open ZacharyGong opened 5 years ago

ZacharyGong commented 5 years ago

Hi, I have trained this model following the settings in your paper (batch size 32, on BSDS dataset, 500 epochs, the lr decay etc), but I found I cannot obtain the same MS-SSIM result mentioned in your paper. Therefore, I used a subset of UCF101 dataset as the training set, which improves the performance. But still, the MS-SSIM result is not satisfying. For example, I got MS-SSIM 0.951 at about 0.44 bpp. As you have mentioned in your paper, models at different bit rates are obtained by fine tuning the final layer of the encoder, while I trained every model from scratch by modifying the numbers channels in the final layer of the encoder. I wonder this might cause a performance gap?

Another question in the compute_bpp function, I found that you used the theoretical lower bound of the entropy to represent the code length, which is a reasonable estimation. However, if we want to compare it with the traditional compression algorithm, like JPEG, which uses Huffman coding, I think we might need the real code length after Huffman coding to calculate bpp for a fair comparison.

Still another question about the PSNR result, which is not mentioned in your paper. In the paper lossy image compression with compressive autoencoders, the trained model can get a PSNR of 35 dB at 1 bpp. While my trained model can only get 30.6 dB at a similar bit rate. I think it is really a huge gap. It is true that the PSNR as an evaluation metric has its limitation, but it is still an important aspect to evaluate a compression algorithm. I wonder if you could share the PSNR result of your trained model? Because I have built and trained several image compression models, I found it is really hard to improve the PSNR result, and I really hope to know the reason.

Looking forward to your reply! Gong

yezongmiao commented 5 years ago

Hi, i have trained this model ,as you said ,i also can't get performance mentioned in author's paper, for 0. 47bpp, MS-SSIM is 0.9022 , can we talk about some training detail ? thanks a lot.

liaopeiyuan commented 5 years ago

Hi, thank you all for your interests in our work. It is true that we've omitted in the difference between the bpp we used with the one used in traditional algorithms. Regarrding PSNR results, I will talk with Haimeng about whether and when we will release them.

ZacharyGong commented 5 years ago

@yezongmiao Hi, I guess you might be interested in the latest work of image compression. Please check this repo from Google.