🎼 Music Classification: Beyond Supervised Learning, Towards Real-world Application

📖 Book

💻Code

📜 Paper

📝Dataset

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🔋 Density-aware Chamfer Distance

Github: density_aware_chamfer_distance

Paper: https://arxiv.org/abs/2111.12702

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

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🛢 Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Github: https://github.com/samb-t/unleashing-transformers

Paper: https://arxiv.org/abs/2111.12701v1

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

Project: https://samb-t.github.io/unleashing-transformers

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📹 End-to-End Referring Video Object Segmentation with Multimodal Transformers

Github: https://github.com/mttr2021/MTTR

Paper: https://arxiv.org/abs/2111.14821v1

Dataset: https://kgavrilyuk.github.io/publication/actor_action/

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🧬 Evolutionary Scale Modeling

Github: https://github.com/facebookresearch/esm

Paper: https://openreview.net/pdf?id=uXc42E9ZPFs

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Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning

Github: https://github.com/JDAI-CV/faceX-Zoo

Paper: https://arxiv.org/abs/2112.00568v1

Dataset: https://paperswithcode.com/dataset/replay-attack

@ArtificialIntelligencedl

🔹 Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning

Github: https://github.com/facebookresearch/Mask2Former

Installation: https://github.com/facebookresearch/Mask2Former/blob/main/INSTALL.md

Paper: https://arxiv.org/abs/2112.01527v1

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

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🔗 DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

Github: https://github.com/raoyongming/denseclip

Paper: https://arxiv.org/abs/2112.01518v1

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

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🦎 → 🐍 NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Github: https://github.com/GEM-benchmark/NL-Augmenter

Paper: https://arxiv.org/abs/2112.02721v1

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

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💉 Semi-supervised-learning-for-medical-image-segmentation.

Github: https://github.com/HiLab-git/SSL4MIS

Paper: https://arxiv.org/abs/2112.04894v1

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🎓 GAN-Supervised Dense Visual Alignment

Github: https://github.com/wpeebles/gangealing

Project: https://www.wpeebles.com/gangealing

Paper: https://arxiv.org/abs/2112.04894v1

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

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Бесплатный онлайн-учебник по ML и Data Science

Для начинающих ML-специалистов, аналитиков и разработчиков появилось отличное учебное онлайн-пособие, которое систематизирует актуальную базовую информацию о Machine Learning и Data Science и помогает погрузиться в тему.

Авторы учебника — специалисты Школы анализа данных Яндекса — проводят от основ машинного обучения и знакомства с ключевыми для ML разделами математики до примеров реального применения их на практике. Учебник выложен в свободный доступ, и сейчас в нем открыты две главы: «Классические методы обучения с учителем» и «Оценка качества моделей». В ближайшее время появятся и новые разделы — авторы обещают регулярно обновлять информацию вслед за развитием сферы ML. Добавляйте в закладки!

🕷 Bayesian Active Learning (BaaL)

Github: https://github.com/ElementAI/baal

Documentation: https://baal.readthedocs.io.

Paper: https://arxiv.org/abs/2112.06586v1

Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source

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📹 Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos

Github: https://github.com/lajlksdf/vtl

Paper: https://arxiv.org/abs/2112.08117v1

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

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📑 Extreme Zero-Shot Learning for Extreme Text Classification

Github: https://github.com/amzn/pecos

Paper: https://arxiv.org/abs/2112.08652v1

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💡 Ensembling Off-the-shelf Models for GAN Training

Github: https://github.com/nupurkmr9/vision-aided-gan

Paper: https://arxiv.org/pdf/2112.09130v1.pdf

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

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🌠 NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques.

Github: https://github.com/netket/netket

Paper: https://arxiv.org/pdf/2112.10526v1.pdf

Homepage: https://www.netket.org

Documentation: https://www.netket.org/documentation

Tutorials: https://www.netket.org/tutorials

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📐 RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (PyTorch)

Github: https://github.com/DingXiaoH/RepMLP

Pre-trained model: https://drive.google.com/drive/folders/1eDFunxOQ67MvBBmJ4Bw01TFh2YVNRrg2?usp=sharing

Paper: https://arxiv.org/abs/2112.11081v1

Task: https://paperswithcode.com/task/semantic-segmentation

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📑 GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Github: https://github.com/openai/glide-text2im

Notebooks: https://github.com/openai/glide-text2im/blob/main/notebooks

Paper: https://arxiv.org/abs/2112.10741

Task: https://paperswithcode.com/task/image-generation

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📹 MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

Github: https://github.com/mseg-dataset/mseg-api

Paper: https://arxiv.org/abs/2112.13762

Dataset: https://paperswithcode.com/dataset/sun-rgb-d

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