​​ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation

The authors suggest ObjectAug perform object-level augmentation for semantic image segmentation.
This approach has the following steps:
- decouple the image into individual objects and the background using the semantic labels;
- augment each object separately;
- restore the black area brought by object augmentation using image inpainting;
- assemble the augmented objects and background;

Thanks to the fact that objects are separate, we can apply different augmentations to different categories and combine them with image-level augmentation methods.


Paper: https://arxiv.org/abs/2102.00221

#deeplearning #augmentation #imageinpainting #imagesegmentation

Call for speakers for Machine Learning REPA Week 2021

ML REPA and LeanDS communities organize an international online conference Machine Learning REPA Week 2021

We are inviting speakers to give talks or workshops on Machine Learning Engineering, Automation, MLOps and Management topics.

CALL FOR SPEAKERS

Conference language: ENGLISH
Dates: 5 - 11 April 2021 (7 pm - 9 pm Moscow time, GMT+3)
Format: Online, zoom
Content: Talks up to 30 min, workshops / demos up to 60 min
Topics: Management, Version Control, Pipelines Automation, MLOps, Testing, Monitoring
Deadline: 15 March 2021

Url to apply: https://mlrepa.com/mlrepa-week-2021

#conference #callforspeakers

Hands on ML notebook series

Updated our ultimate post with a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.


Link: https://github.com/ageron/handson-ml

#wheretostart #opensource #jupyter

neuroplanets
took from the channel https://t.iss.one/NeuralShit

full video: https://www.youtube.com/watch?v=tPyPwW7W1GM

Pie chart naming in different languages.

Credit: https://twitter.com/ElephantEating/status/1360988590814023683

2020 in ML and NLP publications

By conferences, countries, companies, universities and most productive scientists:
https://www.marekrei.com/blog/ml-and-nlp-publications-in-2020/

Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

#Google has released an open source #AutoML framework capable of hyperparameter tuning and ensembling.

Blog post: https://ai.googleblog.com/2021/02/introducing-model-search-open-source.html
Repo: https://github.com/google/model_search

Towards Causal Representation Learning

Work on how neural networks derive casual variables from low-level observations.

Link: https://arxiv.org/abs/2102.11107

#casuallearning #bengio #nn #DL

​​Real-World Super-Resolution of Face-Images from Surveillance Cameras

Most SR methods are trained on LR (low resolution) data, which is downsampled from HR (high resolution) data using bicubic interpolation, but real-life LR images are usually different, so models work worse on them. In this paper, the authors suggest using blur kernels, noise, and JPEG compression artifacts to generate LR images similar to the original ones.
Another suggested improvement is using ESRGAN and replacing VGG-loss with LPIPS-loss, as well as adding PatchGAN.
In addition, the authors show that NIMA metric better correlates with human perception (mean opinion rank) than traditional Image Quality Assessment methods.

Paper: https://arxiv.org/abs/2102.03113

#deeplearning #superresolution #gan #facesuperresolution

Ukrainian Open STT 1000 Hours

Following the path of Open STT in Russian, now you can enjoy a similar dataset in Ukrainian:

- Torrent Link
- GitHub Link

Congratulations to our Ukrainian friends for finally publishing a diverse easily downloadable dataset!

Their pages / dataset UX is still a bit rough on the edges, but compared how fast for example Common Voice accumulates data (130 hours for Russian and 43 hours for Ukrainian), UA Open STT and Open STT remain the best resource for respective languages to date.

Also unlike the majority of STT datasets which are (i) behind a paywall or sponsored by corporations (ii) have limited scope / domains (iii) fit some sort of agenda (i.e. use more GPUs than necessary, use our bloated tools, etc), this dataset is legit made by real people.

Also recently corporations have taken up the trend of rehashing publicly available data, which is cool, but unique data is still nowhere to be seen for obvious reasons (except for Common Voice, which is decent only for English).

#dataset

Recent Advances in Language Model Fine-tuning

By Sebastian Ruder:

https://ruder.io/recent-advances-lm-fine-tuning/

MIT Introduction to Deep Learning

And specifically, lecture about RNN and its modifications:
https://youtu.be/qjrad0V0uJE

The course is excellent as well, but more about image processing. For NLP beginners, such clear and elegant survey about RNNs will be quite useful. So, a lot of architectures in NLP models came from image processing tasks. If you want to recap some theory or get understanding of basics of DL — strong recommendation!

SEER: The start of a more powerful, flexible, and accessible era for computer vision

#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.

And here it is — model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.

Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)

Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf

#facebook #fair #cv #dl

​​Self-training improves pretraining for natural language understanding

Authors suggested another way to leverage unlabeled data through semi-supervised learning. They use #SOTA sentence embeddings to structure the information of a very large bank of sentences.

Code: https://github.com/facebookresearch/SentAugment
Link: https://arxiv.org/abs/2010.02194

​​Contrastive Semi-supervised Learning for ASR

Nowadays, pseudo-labeling is the most common method for pre-training automatic speech recognition (ASR) models, but in the case of low-resource setups and domain transfer, it suffers from a supervised teacher model’s degrading quality. The authors of this paper suggest using contrastive learning to overcome this problem.

CSL approach (Contrastive Semi-supervised Learning) uses teacher-generated predictions to select positive and negative examples instead of using pseudo-labels directly.

Experiments show that CSL has lower WER not only in comparison with standard CE-PL (Cross-Entropy pseudo-labeling) but also under low-resource and out-of-domain conditions.

To demonstrate its resilience to pseudo-labeling noise, the authors apply CSL pre-training in a low-resource setup with only 10hr of labeled data, where it reduces WER by 8% compared to the standard cross-entropy pseudo-labeling (CE-PL). This WER reduction increase to 19% with a teacher trained only on 1hr of labels and 17% for out-of-domain conditions.


Paper: https://arxiv.org/abs/2103.05149

#deeplearning #asr #contrastivelearning #semisupervised

​​Revisiting ResNets: Improved Training and Scaling Strategies

The authors of the paper (from Google Brain and UC Berkeley) have decided to analyze the effects of the model architecture, training, and scaling strategies separately and concluded that these strategies might have a higher impact on the score than the architecture.

They offer two new strategies:
- scale model depth if overfitting is possible, scale model width otherwise
- increase image resolution slower than recommended in previous papers

Based on these ideas, the new architecture ResNet-RS was developed. It is 2.1x–3.3x faster than EfficientNets on GPU while reaching similar accuracy on ImageNet.

In semi-supervised learning, ResNet-RS achieves 86.2% top-1 ImageNet accuracy while being 4.7x faster than EfficientNet-NoisyStudent.

Transfer learning on downstream tasks also has improved performance.

The authors suggest using these ResNet-RS as a baseline for further research.


Paper: https://arxiv.org/abs/2103.07579

Code and checkpoints are available in TensorFlow:
https://github.com/tensorflow/models/tree/master/official/vision/beta

https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs

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


#deeplearning #computervision #sota

​​LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Pre-training transformers simultaneously on text and images proved to work quite well for model performance on multiple tasks, but such models usually have a low inference speed due to cross-modal attention. As a result, in practice, these models can hardly be used when low latency is required.

The authors of the paper offer a solution to this problem:
- pre-training on three new learning objectives
- extracting feature indexes offline
- using dot-product matching
- further re-ranking with a separate model

LightningDOT outperforms the previous state-of-the-art while significantly speeding up inference time by 600-2000× on Flickr30K and COCO image-text retrieval benchmarks.

Paper: https://arxiv.org/abs/2103.08784

Code and checkpoints will be available here:
https://github.com/intersun/LightningDOT

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


#pretraining #realtime #ranking #deeplearning

​​Finetuning Pretrained Transformers into RNNs
Microsoft+Deepmind+...

Transformers is the current SOTA in language modeling. But they come with significant computational overhead, as the attention mechanism scales quadratically in sequence length. The memory consumption also grows linearly as the sequence becomes longer. This bottleneck limits the usage of large-scale pretrained generation models, such as GPT-3 or Image transformers.

Several efficient transformer variants have been proposed recently. For example, a linear-complexity recurrent variant has proven well suited for an autoregressive generation. It approximates the softmax attention with randomized or heuristic feature maps but can be difficult to train or yield suboptimal accuracy.

This work converts a pretrained transformer into its efficient linear-complexity recurrent counterpart with a learned feature map to improve the efficiency while retaining the accuracy. To achieve this, they replace the softmax attention in an off-the-shelf pretrained transformer with its linear-complexity recurrent alternative and then finetune.

➕ Pros:
+ The finetuning process requires much less GPU time than training the recurrent variants from scratch
+ Converting a large off-the-shelf transformer to a lightweight inference model w/o repeating the whole training procedure is very handy in many downstream applications.

📝 arxiv.org/abs/2103.13076

​​Predicting body movement based on data from socks

Researchers from #MIT has opened a new vector of opportunity for #techwear by making smart socks.

Website: https://www.csail.mit.edu/news/smart-clothes-can-measure-your-movements
Video: https://senstextile.csail.mit.edu/file/overview.mp4
Paper: https://www.nature.com/articles/s41928-021-00558-0

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!