pierrefdz Goto Github PK

cogview2

official code repo for paper "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers"

computer_animation

INF585 Computer Animation - C++/ openGL project of an interactive animation of a flying house (boids, cloth animation, etc.)

deit

Official DeiT repository

dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

dna-sequence-classification-with-kernel-methods

Machine Learning with Kernel Methods (MVA 2021) - DNA sequence classification from scratch using kernel methods and ML algorithm

draughts-detector

INF573 Computer Vision - Pawns and grid detection of a draughts game board

hidden

Pytorch implementation of paper "HiDDeN: Hiding Data With Deep Networks" by Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei

latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

mbrs

This is the source code of paper MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression, which is received by ACM MM' 21.

modal-up

INF472F 3D Expressive Modelling - Under the supervision of Pr. Marie-Paule Cani, a javascript program in which the user can model a flying house in the most intuitive manner as possible

neural-painting

Numerical Imaging (MVA 2021) - Project on "Stylized Neural Painting" by Z. Zou et al.

neuro-sequence

Scientific Group Project - NeuroScience experiment developped on React Native

pics.with.ai

Pics with AI website

pierrefdz

pierrefdz.github.io

Personal Website

point-transformer

NPM3D: 3D Modeling & Point Clouds (MVA 2021) - Project on "Point Transformer" by H. Zhao et al.

stable_signature

Please go to https://github.com/facebookresearch/stable_signature

taming-transformers

Taming Transformers for High-Resolution Image Synthesis

train-nn

Code to train distributed neural nets

videomae

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training