Popular GitHub Repositories for Extreme Mtl

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

A PyTorch Library for Multi-Task Learning

2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

"VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos"

[ECCV22] BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering

PECOS - Prediction for Enormous and Correlated Spaces

TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples.

End-to-End speech recognition implementation base on TensorFlow (CTC, Attention, and MTL training)

Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis (ECCV 2024 Oral) - Official Implementation

TRACER: Extreme Attention Guided Salient Object Tracing Network (AAAI 2022) implementation in PyTorch

Datasets collection and preprocessings framework for NLP extreme multitask learning

Extreme Angular is a strict & opinionated starter template with pre-configured ESLint, Prettier, Stylelint, CSpell, Git hooks, CI/CD, and VS Code settings.

X-Transformer: Taming Pretrained Transformers for eXtreme Multi-label Text Classification

Extreme Learning Machine implemented in Pytorch

[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

Adversarial Generation of Extreme Samples

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

Official repository for PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement (WACV 2023)