ichocolatee

AudioSignalProcessingForML

Code and slides of my YouTube series called "Audio Signal Proessing for Machine Learning"

xvector-cnceleb

kaldi based x-vector trained on Cn-Celeb

cs-self-learning

计算机自学指南

SpeakerGuard

a Pytorch library for security research on speaker recognition, released in "Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition" accepted by TDSC

FAKEBOB

Source code for paper "Who is real Bob? Adversarial Attacks on Speaker Recognition Systems" (IEEE S&P 2021)

adversarial-attack-on-GMM-i-vector-based-speaker-verification-systems

Implementation of Adversarial Attacks on GMM i-vector based Speaker Verification Systems (ICASSP2020) https://arxiv.org/abs/1911.03078

learning-to-fool-the-speaker-recognition

code for paper "learning to fool the speaker recognition"

AVIH

Code for Visual Information Hiding Based on Obfuscating Adversarial Perturbations

awesome-audio-plaza

Daily tracking of awesome audio papers, including music generation, zero-shot tts, asr, audio generation

CVAE-GAN-zoos-PyTorch-Beginner

For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.

Explainability-for-Large-Language-Models

RISE

Detect model's attention

cnn-visualization-keras-tf2

Filter visualization, Feature map visualization, Guided Backprop, GradCAM, Guided-GradCAM, Deep Dream

ScaledYOLOv4

Activation-Maximization

Python implementation of activation maximization with PyTorch.

pytorch_D-RISE

A PyTorch implementation of D-RISE

RISE

Implementation of "RISE: Randomized Input Sampling for Explanation of Black-box Models"

torch-cam

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Awesome-explainable-AI

A collection of research materials on explainable AI/ML

interpret

Fit interpretable models. Explain blackbox machine learning.

awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

zihao_course

同济子豪兄的公开课

net2vec

Code for Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

invert

PyTorch implementation of Mahendran & Vedaldi, 2015: "Understanding Deep Image Representations by Inverting Them"

deep-generator-network

Unofficial implementation of "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks" by Nguyen et al.

synthesizing

Code for paper "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

XAI-papers

pytorch-cnn-visualizations

Pytorch implementation of convolutional neural network visualization techniques

awesome-xai

Papers about Explainable AI (Deep Learning-based)

InterpretableMLBook

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版