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Awesome-LLM-Watermark

UP-TO-DATE LLM Watermark paper. 🔥🔥🔥

awesome-llm-security

A curation of awesome tools, documents and projects about LLM Security.

Awesome-GenAI-Watermarking

A curated list of watermarking schemes for generative AI models

knockoffnets

Knockoff Nets: Stealing Functionality of Black-Box Models

Awesome-Diffusion-Models

A collection of resources and papers on Diffusion Models

GradientsScrutinizer

Graph-Adversarial-Learning

A curated collection of adversarial attack and defense on graph data.

ZOO-Attack

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks

Awesome-Learning-Resource

A curated list of all kinds of learning resources, blogs, books, videos and so on.

PFLlib

We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study

awesome-graph-attack-papers

Adversarial attacks and defenses on Graph Neural Networks.

graph-neural-networks.github.io

This repo is for hosting our GNN book titled "Graph Neural Networks: Foundations, Frontiers, and Applications".

oreilly-hands-on-transformers

Hands on NLP and Computer Vision with Transformers

awesome-ml-privacy-attacks

An awesome list of papers on privacy attacks against machine learning

FedLab

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

FedShare

PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

NIID-Bench

Federated Learning Benchmark - Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)

CCL

PyTorch Implementation on Paper [CVPR2021]Distilling Audio-Visual Knowledge by Compositional Contrastive Learning

objects-that-sound

Unofficial Implementation of Google Deepmind's paper `Objects that Sound`

Federated-Learning-for-Human-Mobility-Models

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility

SoundingEarth

Self-supervised Audiovisual Representation Learning for Remote Sensing Data

GDT

We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multimodal-Aerial-Scene-Recognition

Code for <Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition> (ECCV 2020)

awesome-self-supervised-learning

A curated list of awesome self-supervised methods

ssl-transfer

Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"

Cross-and-Learn

Implementation of the Cross and Learn training scheme

XDC

Self-Supervised Learning by Cross-Modal Audio-Video Clustering (NeurIPS 2020)

AVID-CMA

Audio Visual Instance Discrimination with Cross-Modal Agreement

awesome-audio-visual

A curated list of different papers and datasets in various areas of audio-visual processing

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications