For question and feedback about "InfoMin", please leave it here.

[HobbitLong]

Firstly, my apology that this repo currently contains much more beyond "InfoMin", and that the view learning experiments are not released here. I just realized that I should have a separate repo for "InfoMin" to host fun experiments. I will do this in the future.

If you have specific question about "InfoMin" paper, you could leave it under this issue.

[WOWNICE]

Hey Amazing work.

But I think there might be a typo in proposition 4.1, where

"(v_1*, v_2*) = \min_{v_1, v_2}I(v_1; v_2)"

should be

"(v_1*, v_2*) = \arg \min_{v_1, v_2}I(v_1; v_2)".

Thanks for your amazing work again.

[HobbitLong]

Hey @WOWNICE, Thank you very much for spotting this!

I have fixed it and will update it to the next version.

[alldbi]

Hi, thank you for the code. It is great.

Unfortunately, I can't find the part in infomin that learns views using the flow model and the min/max game. Is it implemented yet?

[HobbitLong]

Hi, @alldbi,

Thanks for your interest! The view learning experiments will be released in a separate repo later.

[alldbi]

@HobbitLong Thank you very much!

[haohang96]

Is there an official InfoMin configs (detail args) provided for 73.0% results? Thanks！

[haohang96]

In addition, can you provide the speed information when train infomin? Thanks very much!

[HobbitLong]

@haohang96,

Is there an official InfoMin configs (detail args) provided for 73.0% results?

Appending --epochs 800 --cosine besides specifying --method InfoMin

In addition, can you provide the speed information when train infomin?

I did not remember the exact time but will get back if I tested. But roughly, I think it will take about two weeks to train 800 epochs with 8 Titan V100.

[amsword]

From the paper, RandAugmentation is used for pre-training. are the parameters of rand_augmentation inherited from the imagenet supervised classification task? or are the parameters from the parameter searching based on the task of pre-training + fine-tuning?

[HobbitLong]

From the paper, RandAugmentation is used for pre-training. are the parameters of rand_augmentation inherited from the imagenet supervised classification task? or are the parameters from the parameter searching based on the task of pre-training + fine-tuning?

@amsword, I did not explicitly search it, but of course I have been inspired by the setting in the original RA paper. The way I initially got the parameter was doing a coin toss over [1, 2] for number of layer, and randomly chose a magnitude from [5, 10, 15], resulting in what you see from the code now.

Later on, I did do a quick validation on ImageNet-100 subset (e.g., trying magnitude of 5, 10, 15, 20), and it seems to me that the layer and magnitude do not matter that much, what matters is that you just need to have it. So I stick to my initial random setting. But since I didn't search over the whole ImageNet, the current setting could be sub-optimal.

[alceubissoto]

Hi @HobbitLong! Thanks for sharing your code for the current and previous papers.

If I understood correctly, in the current paper (and also on CMC) when using the Ydbdr color space, the Y channel would compose the first view, while Dbdr would compose the second one. If so, I couldn't find in the code where or how the channels are split to compose each view. May you please help me?
Thanks in advance!

[HobbitLong]

Hi, @alceubissoto,

For color space transferring, it's here.

For channel splitting, it's here.

Let me know if you have further question.

[xwang-icsi]

Thanks for your excellent work! I have a question regarding the linear evaluation of infoMin. I noticed that you are using Random Augmentations (RA) in linear evaluation stage, whether the experimental results in your infoMin paper is also produced with RA? Thank you!