Как выявлять аномалии в разных распределениях с помощью машинного обучения? 🧐

10 марта в 20:00 (мск) пройдет открытый вебинар «Anomaly Detection». Его проведет Артем Васильев, ведущий инженер разработки. С экспертом мы обсудим такие вопросы, как постановка задачи, нахождение аномалий в разных распределениях, SVD-feature extraction, Autoencoder, PaDiM.

🔥 Продолжить получать новые знания вы можете на онлайн-курсе «Компьютерное зрение» для специалистов в сфере Machine Learning, которые хотят специализироваться на компьютерном зрении или систематизировать свои знания.

Чтобы участвовать, зарегистрируйтесь 👉 https://otus.pw/pd5h/

Official PyTorch implementation for Graph Matching based GNN Pre-Training.

Github: https://github.com/rucaibox/gmpt

Paper: https://arxiv.org/abs/2203.01597v1

@ArtificialIntelligencedl

Uncertainty Estimation for Heatmap-based Landmark Localization

Github: https://github.com/pykale/pykale

Documentatuin: https://github.com/pykale/pykale

Paper: https://arxiv.org/abs/2203.02351v1

Dataset: https://paperswithcode.com/dataset/kitti

@ArtificialIntelligencedl

🧊 DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Github: https://github.com/IDEACVR/DINO

Paper: https://arxiv.org/abs/2203.03605

Dataset: https://paperswithcode.com/dataset/coco

@ArtificialIntelligencedl

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Github: https://github.com/pclucas14/aml

Paper: https://arxiv.org/abs/2203.03798v1

@ArtificialIntelligencedl

Restoring and attributing ancient texts using deep neural networks

Github: https://github.com/deepmind/ithaca

Paper: https://www.nature.com/articles/s41586-022-04448-z

Colab: https://colab.research.google.com/github/deepmind/ithaca/blob/master/colabs/ithaca_inference.ipynb

@ArtificialIntelligencedl

On Embeddings for Numerical Features in Tabular Deep Learning

Github: https://github.com/yura52/rtdl

Documentatuin: https://yura52.github.io/rtdl

Paper: https://arxiv.org/abs/2203.05556v1


@ArtificialIntelligencedl

Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement

Github: https://github.com/xuxw98/backtoreality

Paper: https://arxiv.org/abs/2203.05238v1

Dataset: https://paperswithcode.com/dataset/modelnet

@ArtificialIntelligencedl

Conditional Prompt Learning for Vision-Language Models

Github: https://github.com/kaiyangzhou/coop

Paper: https://arxiv.org/abs/2203.05557v1

Dataset: https://paperswithcode.com/dataset/imagenet

@ArtificialIntelligencedl

Enhancing Adversarial Training with Second-Order Statistics of Weights

Github: https://github.com/alexkael/s2o

Paper: https://arxiv.org/abs/2203.06020v1

Dataset: https://paperswithcode.com/dataset/cifar-10

@ArtificialIntelligencedl

Accelerating DETR Convergence via Semantic-Aligned Matching

Github: https://github.com/ZhangGongjie/SAM-DETR

Documentatuin: https://cocodataset.org/

Paper: https://arxiv.org/abs/2203.06883v1


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Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

Github: https://github.com/davidzhangyuanhan/bamboo

Project: https://opengvlab.shlab.org.cn/bamboo/home

Paper: https://arxiv.org/abs/2203.07845

@ArtificialIntelligencedl

Context-Aware Drift Detection

Github: https://github.com/SeldonIO/alibi-detect

Project: https://docs.seldon.io/projects/alibi-detect/en/latest/

Paper: https://arxiv.org/abs/2203.08644v1

Dataset: https://paperswithcode.com/dataset/imagenet

@ArtificialIntelligencedl

➡️ One-Shot Adaptation of GAN in Just One CLIP

A novel single-shot GAN adaptation method through unified CLIP space manipulations.

Github: https://github.com/submission6378/oneshotclip

Paper: https://arxiv.org/abs/2203.09301v1

Dataset: https://paperswithcode.com/dataset/ffhq

Adapted models: https://drive.google.com/drive/folders/1svLJjuuK-yCCJ7Xq9l4Dy4gSuzplK_7i?usp=sharing

@ArtificialIntelligencedl

🔹 TensoRF: Tensorial Radiance Fields

A novel approach to model and reconstruct radiance fields

Github: https://github.com/apchenstu/TensoRF

Paper: https://arxiv.org/abs/2203.09517v1

Dataset: https://paperswithcode.com/dataset/ffhq

Project page: https://apchenstu.github.io/TensoRF/

https://t.iss.one/ArtificialIntelligencedl

💬 Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation


Github: https://github.com/mgaido91/FBK-fairseq-ST

Paper: https://arxiv.org/abs/2203.09866v1

Dataset: https://paperswithcode.com/dataset/winobias

Project page: https://apchenstu.github.io/TensoRF/

@ArtificialIntelligencedl

🔎 Полное собрание шпаргалок по машинному обучению, статистике, Python и R

Article

@ArtificialIntelligencedl

🪄 Generative Invariance Transfer

Github: https://github.com/allanyangzhou/generative-invariance-transfer

Paper: https://arxiv.org/abs/2203.09739v1

Dataset: https://paperswithcode.com/dataset/cifar-10

@ArtificialIntelligencedl

🚀 AOT (Associating Objects with Transformers for Video Object Segmentation) in PyTorch

Github: https://github.com/yoxu515/aot-benchmark

Paper: https://arxiv.org/pdf/2203.11442v1.pdf

Dataset: https://paperswithcode.com/dataset/davis

Demo: https://github.com/yoxu515/aot-benchmark/blob/main/datasets/Demo

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📑 PubTables-1M: Towards comprehensive table extraction from unstructured documents

Github: https://github.com/microsoft/table-transformer

Paper: https://arxiv.org/pdf/2110.00061.pdf

Dataset: https://msropendata.com/datasets/505fcbe3-1383-42b1-913a-f651b8b712d3

https://t.iss.one/ArtificialIntelligencedl

🦾 Global Tracking Transformers

Github: https://github.com/xingyizhou/GTR

Demo: https://github.com/facebookresearch/detectron2/blob/main/GETTING_STARTED.md

Paper: https://arxiv.org/abs/2203.13250v1

Dataset: https://paperswithcode.com/dataset/mot17

https://t.iss.one/ArtificialIntelligencedl