Popular GitHub Repositories for Shape Bias

Fit interpretable models. Explain blackbox machine learning.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Awesome work on hand pose estimation/tracking

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Deep learning in Rust, with shape checked tensors and neural networks

Official code of "HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation", CVPR 2021

Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

[ICLR'23 Spotlight & IJCV'24] MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

XAI - An eXplainability toolbox for machine learning

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Gated-Shape CNN for Semantic Segmentation (ICCV 2019)

BladeDISC is an end-to-end DynamIc Shape Compiler project for machine learning workloads.

Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

A high-fidelity 3D face reconstruction library from monocular RGB image(s)

Curated list of open source tooling for data-centric AI on unstructured data.

convert mmdetection model to tensorrt, support fp16, int8, batch input, dynamic shape etc.

Shape and dimension inference (Keras-like) for PyTorch layers and neural networks

[NeurIPS 2018] Visual Object Networks: Image Generation with Disentangled 3D Representation.

Notes for Deep Learning Specialization Courses led by Andrew Ng.