paper: https://arxiv.org/pdf/1907.10786.pdf

code: https://github.com/ShenYujun/InterFaceGAN

youtube video: https://www.youtube.com/watch?v=uoftpl3Bj6w

project page: https://shenyujun.github.io/InterFaceGAN/

I’m working on a model with heaps of hyperparameters. It is infeasible to test all combinations so I’ve come up with an attempt to tuning, but I don’t know whether the method is valid. Say I have hyperparameters A, B, and C, each with 3, 4 and 5 options each. Now my plan is to set a baseline, say A:1, B:1, C:1. Then I vary the options of A keeping B and C constant. Hypothetically A:3, B:1, C:1 beats the initial baseline. Now I set A:3, B:1, C:1 to be my new baseline and I vary hyperparameter B. I repeat this process until all parameters have been varied. Then I start out with A again. The assumption here is that hyperparameters influence the performance which I know not to be true.

Can this method be seen as a genuine attempt to tuning? If it is: does anyone know of any references where this or a similar tuning method is used? If not: is there a better method? Furthermore, I’d like to know how you deal with having a lot of hyperparameters.

A full description of the new feature engineering optimization capabilities can be found in this Medium story.

TL;DR: HyperparameterHunter 3.0 adds support for feature engineering optimization. Define different feature engineering steps as normal functions, then let HyperparameterHunter keep track of the steps performed for Experiments, so you can optimize them just like normal hyperparameters, and learn from past Experiments automatically.

HyperparameterHunter is a scaffolding for ML experimentation and optimization. Run one-off Experiments or perform hyperparameter optimization, and HH automatically saves the model, hyperparameters, data, CV scheme, and now feature engineering steps, along with much more. Future optimization will scour your saved Experiments for those compatible with the current search space and use them to automatically jump-start learning.

• Stop keeping janky lists of all your Experiments’ conditions and results
• Ensure optimization actually has sufficient data to be useful
• Let no Experiment be wasted

If you love HyperparameterHunter, I’d like to ask you for your support (yes, you, the attractive one reading this). Starring our GitHub repo, applauding the Medium story, and telling your friends (or enemies) about HyperparameterHunter would be very much appreciated!

If you’d like to do more and offer some feedback, open an issue, or contribute code, I would treasure the opportunity to learn from experts such as yourselves!

Crosspost to /r/DataScience I am deeply worried about automation personally and it is one of the driving reasons. On par with climate change that I don’t want to have any children. My question is: Is it possible to start an open letter from people working on the automation of the most common jobs in America. If we are actually automating these jobs I think this could help build actual awareness and recognition from the media.

Some say it’s not possible to see job loss from automation. That other work “we can’t imagine” will take their place. I hate to sound pessimistic but I don’t really see that happening and/or I don’t see the call center worker or the truck driver being able to retrain for the “jobs of the future.”

I saw someone in a different sub suggest an open letter where professionals could sign in support of the fact that this is in fact happening. None of the political candidates are talking about this. This is as important as the climate, if we have 25% unemployment riots I think that’s going to be as bad or worse than rising sea levels. Do you think this could get traction? Or am I totally off base, sorry I’m not a professional just a guy worried about the future.

The human decision-making is heavily influenced by beliefs, biases and heuristics. The decision-making in (inductive) machine learning algorithms is grounded in inductive biases. I was wondering if we can establish a connection between both. In particular, I am interested in bridging the gap for the following example:

Say, we have a few lego blocks on the table that are randomly arranged. If we ask us humans to move one block passed another without interfering with it, we will analyze structure in the perceptual input and decompose the scene into entities, relations and relational constraints. We will also access our knowledge/models about objects (Sperkle et al., “Core knowledge”) and will use our beliefs and experience to find an appropriate solution.

Now, I would argue that if we ask the same thing a robot, we would require similar decision-making capabilities. I would further argue that we require two key components:

1. a forward model of objects or the scene (to “hallucinate” consequences)
2. A relational inductive bias that allows to exploit structure and impose constraints on relations and interactions of entities during learning the forward model.

Assuming my assumptions are correct, I was wondering if I can make a connection between human decision-making and such a machine intelligence model. Is there something similar in human decision-making for the decomposition of a scene into entities and relations that is related to a machine learning (inductive) bias?

Thanks a lot!

Hi all /r/machinelearning.

I’ve recently looked for the previous researches on evaluation model for franchises, credit status and etc, where a target has many activities (like transaction, order, etc). Credit status of a person is one good example. It has the transaction history and income info and can be used as dataset.

I couldn’t find the one that seems fit to this subject. In my mind I may used a wrong keyword to find. What is the name of this field? It is not certainly NLP, or Image…

I will appreciate for any paper you recommend or keywords to search through google, or even the subject name of the field.

Hi,

The default configurations of RCNN or Faster-RCNN are designed for VOC data-set. The data-set has many classes, and objects of different class are of different size. So, I think it is necessary to use different scales and zoom levels.

But in pathology images, all images are on same scale. Even the nuclei are comparable. So is there any particular configuration we should use for nucleus detection?
Any intuition and advice would be helpful.

Hi All,

​

CycleGAN and DiscoGAN are very similar in their functionality and seem to be concurrent works. The loss function of CycleGAN is L1 loss while DiscoGAN uses MSE. CycleGAN has an additional identity loss function.

​

While CycleGAN produces impressive results on horse2zebra, it seems to fail at the task of cat2dog (geometric transformation). DiscoGAN, on the other hand, is able to perform the task of Handbags2Shoes.

​

TL;DR: What makes DiscoGAN perform the geometrical transformation better than CycleGAN ? Is it the network architecture or the MSE loss function or is there is a secret sauce ?

Using a pre-trained network for your new task seems standard practise. I’m talking about the case where we retrain the entire network on the new task. Not only the final layers. What are you all thinking on how this improves performance on new tasks? Why cannot we learn those exact representations on the new task?

My own reasoning gets stuck at the following point: usually we pretrain the network on a larger data set. The network learns representations on that large data set that it could not have learned on our own smaller data set. The small data set might not have enough evidence for those representations. However, using those parameters actually improves performance. So the associated representations are actually useful. That seems contradictory.

I find others frequently asking about different annotation tools for labeling custom datasets. Many tools are being developed all the time, so I created a awsome list to help myself and others find the proper dataset tools they are looking for.

https://github.com/jsbroks/awesome-dataset-tools

All the best on your data hunts.