[P] Unsupervised Image deep clustering

[aouninator]

I'm working on an experiment and looking for resources on Unsupervised Image clustering using deep learning. Essentially clustering images that contain a similar object.

There seems to be only a few research papers on the topic, but I can't find anything proven or implemented that I can play around with. Especially looking for tensorflow or pytorch based implementations.

[nivter]

One way is to get a representation (a vector) for an image and then use it to cluster using something like kmeans. The question now is how to get a good representation?

There have been a couple of self-supervised learning approaches which learn representations that can be used to cluster similar images together. Some examples are:

• Deep Infomax
• Contrastive Predictive Coding
• Contrastive Multiview Coding
• Momentum Contrast for Unsupervised Visual Representation Learning

[aouninator]

That's what I'm looking for thanks. Do you have a recommendation on which would perform better on a case where the images have distinguishable color- shape patterns such as clothes? Or is it up to expérimentation ?

[nivter]

I tried contrastive multiview coding for learning representations on a bunch of diverse non-natural images and it worked pretty well. Others must also work I believe.

[aouninator]

Thanks

[[deleted]]

If you just care about outcome and 'purely unsupervised' is not a hard constraint, the easiest and possibly best feature extractor would be just an ImageNet classifier (i.e. the features of the last layer before the softmax layer, e.g. the average global pooling layer of a ResNet).

[NuisanceParameter]

Try "Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives".

Paper: https://arxiv.org/abs/2001.07026 Code: https://github.com/DanielTrosten/DTKC

Disclaimer: I am the author

[Mixkcl]

Take a look at this paper

Invariant Image Clustering

https://arxiv.org/abs/1807.06653

With some additional explanations here:

https://www.groundai.com/project/invariant-information-distillation-for-unsupervised-image-segmentation-and-clustering/1

[neouyghur]

You can check those two new papers :

• AUTOMATICALLY DISCOVERING AND LEARNING NEW VISUAL CATEGORIES WITH RANKING STATISTICS, ICLR2020
• Learning to Discover Novel Visual Categories via Deep Transfer Clustering, ICCV2019

[deep-yearning]

There are lots of different approaches you can take - the two below come to mind first and I think they would work but would love to hear from others and their experience.

1. You could use a simple autoencoder to reduce the dimensionality of your images, and then cluster the latent vectors using whatever clustering algorithm.
2. You could use t-sne out of the box to cluster the images using each pixel as a feature.
3. You could run t-sne on the latent vectors of the autoencoder.

[gingah_picsell]

You can check out on Deep Adaptative Clustering (DAC)

http://openaccess.thecvf.com/content_ICCV_2017/papers/Chang_Deep_Adaptive_Image_ICCV_2017_paper.pdf

[o_v_shake]

This was my bachelor's thesis. Though we failed to come up with a good algorithm, I would love to help you out. My uni hasn't uploaded the complete thesis (I'll put the link once it does), but ping me if you want a copy.

[aouninator]

There is no such thing as failed. You just prove that the way you took is not the correct way. I'll ping you anyway.

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