Natural Language Processing Best Practices & Examples
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Updated
Aug 30, 2022 - Python
Natural Language Processing Best Practices & Examples
SOTA Re-identification Methods and Toolbox
Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
Shape and dimension inference (Keras-like) for PyTorch layers and neural networks
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch. Find explanation at tourdeml.github.io/blog/
A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS
Official repository of the paper "HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment".
State-of-the-art methods on monocular 3D pose estimation / 3D mesh recovery
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
A collection of SOTA Image Classification Models in PyTorch
A state of art detector for densely packed scenes dataset SKU-110K
A Gluon implement of Residual Attention Network. Best acc on cifar10-97.78%.
SOTA Google's Perceiver-AR Music Transformer Implementation and Model
PVT: Point-Voxel Transformer for 3D Deep Learning
Multi-agent PPO with noise (97% win rates on Hard scenarios of SMAC)
[SOTA] [92% acc] 786M-8k-44L-32H multi-instrumental music transformer with true full MIDI instruments range, efficient encoding, octo-velocity and outro tokens
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