Hi All,

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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.

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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.

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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.

I think building a model to learn a specific musician’s style could be a fun side project and was hoping to get some thoughts. I have complete multi-track MIDI discographies for a few bands, but I’m struggling to figure out the best way to structure the input/output of the model.

The general idea is to feed in some set of instrument/vocal tracks (this selection will basically be a hyperparameter) and generate the target track as an output. An RNN seems like the obvious approach and I was planning to start with a BLSTM using Keras and music21 in Python. To generate the training data, I’ll use a training set of the songs to randomly sample 10 second (another hyperparameter) clips. The same track will always be the target, and I am planning to use a consistent subset of the other tracks as the input. However, it would be nice to eventually use a varied set of input tracks to generate the output.

Does anyone have experience doing something similar to this, or have any relevant papers/libraries to recommend?

I hope I am positing in the right place. If not, i’ll take the post down.

I am a university researcher working on a project that involves matching names with biographical data from SEC filings. I downloaded all the filings for the organizations I am interested in, and wrote a Python script that basically finds officers’ names in the document, and then looks for gender, age, education and job title. It is tricky because companies can have different formats for these documents, so you have to think about different possibilities the data can be presented.

I use a fuzzy string matching to account for differences in spelling and typos, and different ways of naming tables. But most of the “learning” came from me manually tuning the script. Unfortunately, the script has to do a lot of safety checks to avoid outputting jibberish data (e.g. assuming a list of company names are people, etc). Finding age is also very tricky, as you often have to parse sentences to search for patterns such as “Mr. ABC, age 56” or “Mrs. Jean B. XYZ, MD, 46.”

My script works well and outputs the data that I want. The main issue that I have is that it takes a good 30 seconds for all the calculations to be made for just one company (around 5-20 executives per company). The reason, I think, is because my script tests a lot of different possibilities, even if they are not applicable to the document.

I am sure I am not the only one working on extracting data from semi-structured documents. I wanted to know if I could get feedback on what strategies I could implement to improve my script. I would particularly be interested in methods that involve tracking the performance of each data parsing strategies so that the computer does not waste time using a method that doesn’t work so well.

Thanks a lot for your help!

Link: https://distill.pub/2019/advex-bugs-discussion/

Hi, one of the original paper authors here! Recently, Distill.pub hosted a community discussion of our paper, with commenters who reproduced, extended, and further discussed the results of our recent paper. Happy to answer any questions!

I’m working in IT (think somewhere between sysadmin/security and operations) at WWF Netherlands. A colleague of mine who’s involved in the ‘Early Warning System’-program asked me to help me spread the following.

WWF would like to invite you to participate in a Request for Proposals (RfP) to become a tech partner in the Early Warning System (EWS) program which entails the development of a machine learning model to predict the risk of illegal deforestation using big data.

The RfP process began August 5th. Please have a look at the full RfP with technical details on the website and the Terms and Conditions. We encourage you to submit your application when ready as we will be reviewing on an ongoing basis.

If this is not appropriate let me know! If you have questions, ask.. If you have really difficult question, still ask but I might have to check with one of my colleagues 🙂 Do not hesitate to share this, let’s use tech for good.

Hi everyone, we recently open source our NLP/speech project at ACL 2019 – DELTA, a DEep Learning based language Technology plAtform (https://github.com/didi/delta). It aims to provide an end-to-end solution for training, deploying, and developing natural language processing and speech models for both academia and industry use cases.

DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps AI developers to quickly build models and easily deploy to production, featuring:

• Easy-to-use
  • One command to train NLP and speech models, including:
    • NLP: text classification, named entity recognition, question and answering, text summarization, etc
    • Speech: speech recognition, speaker verification, emotion recognition, etc
  • Use configuration files to easily tune parameters and network structures
• Easy-to-deploy
  • What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph
  • Uniform I/O interfaces and no changes for new models
• Easy-to-develop
  • Easily build state-of-the-art models using modularized components
  • All modules are reliable and fully-tested

For more details, check out: https://github.com/didi/delta. Thank you!

I’m training a CNN on high resolution satellite imagery. Because of hardware constraints, I’m using EfficientNetB0 (EB0) to predict classes from 800x800x3 tiles. This can be done thanks to the 2D GlobalAveragePooling layer at the end of the model which compresses the 25x25x1280 feature map (7x7x1280 when working with the original 224×224 images) into a 1D 1280 vector to be fed to dense layers and such. It works surprisingly well. Even more, I get better performance with EB0 used on 800x800x3 tiles than with EB5 applied on 456x456x3 tiles (the resolution for which they were designed).

How is this possible?