Hi, I’ve recently read Data Augmentation by Pairing Samples for Images Classification and found SamplePairing fascinating. I applied the method on my small image dataset and it showed lower validation loss than the baseline 🙂

post: https://jsideas.net/samplepairing/

github: https://github.com/junkwhinger/SamplePairing

Dear Machine Learning SubReddit,

​

Good day, I am a student in UTAR Kampar Malaysia. Currently I am conducting a study/project on the usability issues associated with Big Data and it would be ideal if I can get the input from experienced people in regards to Big Data. With that said here is a link to the survey that I am conducting. “https://forms.gle/J2kLth11eYPNyNUC7” Thank you for reading this and much appreciation to the respondents. As a special thanks I will include the organisations name(or not if you don’t wish to) in my thesis upon completing it. Again I wish to say thank you for reading and I bid you a good day.

​

PS. I am sorry if this make it seem like I am begging but I don’t really have anywhere else to go for respondent to such highly specific question. If you know a better place to post this kind of request please don’t hesitate to pm me or comment on this post.

​

Regard,

Graduating Student, a fellow redditor.

https://github.com/Kyubyong/g2pC

​

There are several open source libraries of Chinese grapheme-to-phoneme conversion such as python-pinyin or xpinyin. However, none of them seem to disambiguate Chinese polyphonic words like “行” (“xíng” (go, walk) vs. “háng” (line)) or “了” (“le” (completed action marker) vs. “liǎo” (finish, achieve)). Instead, they pick up the most frequent pronunciation. Although that may be a simple and economic strategy, machine learning techniques can be of help here. We use CRF to determine the pronunciation of polyphonic words. In addition to the target word itself and its part-of-speech, which are tagged by pkuseg, its neighboring words are also featurized.

I’m trying to find literature on generating samples from a VAE where a prior is learned (as an additional later step after training the VAE) on the latent variables. Then the prior is sampled to produce the latent variables, z, to feed to the decoder, rather than getting z ~ N(0, 1) (if q(z|x) is a unit Gaussian).

​

Empirically I’ve noticed that for very complex and diverse images, the reconstructions from a traditional VAE look good enough (despite being blurry but that’s a different issue), but when you try to generate entirely new samples by feeding the decoder a random z ~ N(0, 1), they look awful.

​

What if we don’t just randomly sample z ~ N(0, 1), but produce robust new z’s in some other way, such as through a function approximator. For example, after training the VAE, go through your entire dataset, compute mu and sigma, and train an auto regressive feedforward model to produce a plausible z. Or use the same data to train a model that takes uniform random noise as input and produces z’s as output. This thought is inspired by the VQ-VAE approach (https://arxiv.org/abs/1711.00937, and https://arxiv.org/abs/1906.00446), where they train a PixelCNN on all the quantized latent vectors produced by the VAE from their entire dataset, to sample new latents and produce new images.

​

Would love to hear your thoughts, or get links to papers on this. Thanks!

Hi everyone,

Several times while dealing with huge files I struggled when I wanted to shuffle those for instance for training. Some methods suggest to split the files and shuffle those separately however it is not a real shuffle (element in bucket 0 will not appear in bucket 10 for instance).
I’ve made a library a few months ago that allow to do this by shuffling the index of the number of lines and reading as much as it is necessary the original file (with a certain batch size) in order to be able to complete the shuffle.

Quick example:
file with 10K lines and batch_size 5k.
– Shuffle index to index_shuffled
– Read in streaming the file and dump the first 5k of index_shuffled
– Read in streaming the file once a gain and dump the last 5k of index_shuffled

Reading a file is not costly that’s why the perfs seem to me quite interesting.

It is really not that complicated but I did not find it available somewhere…

Here is the link: https://github.com/YaYaB/shuffle-big-file

I hope it can be useful to some of you 🙂

Best,

YaYaB.

We’ve implemented an extensive pip package for Conditional Density Estimation that, among other features, includes Mixture Density Network, Kernel Mixture Network, Normalizing Flow Estimator and various non-parametric/semi-parametric estimators (CKDE, NKDE, LSKDE), data simulators and evaluation functions (centered moments, KL/JS divergence, Hellinger distance, percentiles etc.).

The package is constantly improved and we also provide a benchmark & best practices report and a code documentation.

Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation

Benchmark and best practices paper for NN-based CDE: https://arxiv.org/abs/1903.00954

Code docs: https://freelunchtheorem.github.io/Conditional_Density_Estimation/docs/html/index.html

We’re open for suggestions and feedback so please feel free to use & comment. Lastly, if you like our project, we’d be happy if you spread the word and star the GH repo.

Hi everyone!

My team and I have just released a computer vision algorithm that can detect “thumb up” and “thumb down” gestures.

The algorithm is based on tiny-YOLOv3 architecture. Accuracy of thumb up/down gesture recognition is calculated as mean average precision (mAP@0.25) = 85.19%; average IoU = 73.89%.

The neural network was trained on 3000 images.

To try out the algorithm, download it from the GitHub and install it.

We would like to hear your feedback so we can continue to improve our service to you 🙂