Data Science by ODS.ai 🦜

2021-04-09 20:10:17 Starting -1 Data Science Breakfast as an audio chat Open / Comment

2021-04-08 08:20:17 Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains

This is an interesting paper about learning and combining representations of object shape and appearance from the different domains (for example, dogs and cars). This allows to create a model, which borrows different properties from each domain and generates images, which don't exist in a single domain.
The main idea is the following:
- use FineGAN as a base model;
- represent object appearance with a differentiable histogram of visual features;
- optimize the generator so that images with different shapes but similar appearances produce similar histograms;

Paper: https://openreview.net/forum?id=M88oFvqp_9
Project link: https://utkarshojha.github.io/inter-domain-gan/
Code will be available here: https://github.com/utkarshojha/inter-domain-gan

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-furrycars

#cv #gan #deeplearning #contrastivelearning Open / Comment

2021-04-07 18:54:26 Advanced Database Systems

This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both high-performance transaction processing systems (OLTP) and large-scale analytical systems (OLAP).

YouTube Playlist

#database #db #sql #nosql Open / Comment

2021-04-06 20:20:11 Conversational AI Reading List

List of interesting papers as well as some link to the lectures from Conversational AI course for Columbia University:

https://docs.google.com/spreadsheets/u/0/d/1nSKcnM5r9x82BdyPgn-obN1sRUlLC7zZ082a0132Igk/htmlview#gid=1523499517 Open / Comment

2021-04-06 19:24:26 Open / Comment

2021-04-05 13:39:21 Self-supervised Learning for Medical images

Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.

GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680

#biolearning #medical #dl #pytorch #keras Open / Comment

2021-04-02 11:57:10 ​​EfficientNetV2: Smaller Models and Faster Training

A new paper from Google Brain with a new SOTA architecture called EfficientNetV2. The authors develop a new family of CNN models that are optimized both for accuracy and training speed. The main improvements are:

- an improved training-aware neural architecture search with new building blocks and ideas to jointly optimize training speed and parameter efficiency;
- a new approach to progressive learning that adjusts regularization along with the image size;

As a result, the new approach can reach SOTA results while training faster (up to 11x) and smaller (up to 6.8x).

Paper: https://arxiv.org/abs/2104.00298

Code will be available here:
https://github.com/google/automl/efficientnetv2

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-effnetv2

#cv #sota #nas #deeplearning Open / Comment

2021-03-29 15:46:43 ​​Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
The authors suggest several practical ideas to improving model performance on this task:
- using augmentations (synonym replacement, random insertion, random swap, random deletion) together with triplet loss
- using curriculum learning (two-stage and gradual)

Paper: https://arxiv.org/abs/2103.07552

Code: https://github.com/jasonwei20/triplet-loss

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlptriplettricks


#deeplearning #nlp #fewshotlearning #augmentation #curriculumlreaning Open / Comment

2021-03-29 13:43:40 Silero TTS Released

Surprise! A quick pre-release of Silero Text-to-Speech models!

Speakers

10 voices (each available in 16 kHz and 8 kHz):

- 6 Russian voices;
- 1 English voice;
- 1 German voice, 1 Spanish voice, 1 French voice;

Why is this Different?

- One-line usage;
- A large library of voices;
- A fully end-to-end pipeline;
- Naturally sounding speech;
- No GPU or training required;
- Minimalism and lack of dependencies;
- Faster than real-time on one CPU thread (!!!);
- Support for 16kHz and 8kHz out of the box;

Links

- Try our TTS models here;
- Quick summary;
- Performance benchmarks;

Stay tuned for much more detailed PR releases and torch.hub release soon! Open / Comment

2021-03-27 13:51:33 Open / Comment