Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)

"Denoising Diffusion Models for Plug-and-Play Image Restoration", Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, Luc Van Gool.

[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

This repo explain how qr codes works, qr detection and decoding.

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

Biometric Authentication using PPG signals

End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)

A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization

Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning

Allows you to run machine learning models locally on your ESP32 device.

[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

CVPR 2022 HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

MAE for CIFAR，由于可用资源有限，我们仅在 cifar10 上测试模型。我们主要想重现这样的结果：使用 MAE 预训练 ViT 可以比直接使用标签进行监督学习训练获得更好的结果。这应该是自我监督学习比监督学习更有效的数据的证据。

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the …

A PyTorch-based Speech Toolkit

This repository contains audio samples and supplementary materials accompanying publications by the "Speaker, Voice and Language" team at Google.

This repository implements the published algorithm on Transaction on Information Forensics and Security Journal about Cross-Domain Person Re-Identification

Train the network with the Triplet loss function and create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn fea…

Text Independent Speaker Verification Using GE2E Loss

🔈 Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

Differentiable fast wavelet transforms in PyTorch with GPU support.

Non-linear Bayesian filtering for EMG Enveloppe Extraction

C++ implementation of Butterworth filter.

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners