[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection

Implementation of Papers on Adversarial Examples

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Differentiable Optimizers with Perturbations in Pytorch

[CVPR 2018] Tensorflow implementation of NAG : Network for Adversary Generation

NPI Ephemeris Propagation Tool with Uncertainty Extrapolation

[ICLR'24] Official PyTorch Implementation of ContraLSP

Universal Adversarial Audio Perturbations

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"

Building a multi-label classifier from scratch and using transfer learning for the PASCAL VOC image dataset.

Space Engineering 3 Course Work at University of Sydney

Adversarial Attack using a DCGAN

Code to analyze high-density EEG and concurrent EMG/EOG datastreams during balance perturbations (replicates results from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088363/)

Dark photon conversions in our inhomogeneous Universe. Code repository associated with the papers https://arxiv.org/abs/2002.05165 and https://arxiv.org/abs/2004.06733.

A deep convolutional neural network is used to explain the results of another one (VGG19).

Single-Cell (Perturbation) Model Library

A Character-level Perturbation Generator based on probability distribution, density and diversity.

The MATLAB model here presented performs trajectory propagations based on the Constant Density Polyhedron algorithm. With this model, it is possible to compute trajectories in the proximity of astronomical bodies such as asteroids or comets. The algorithm also allows to compute ballistic trajectories.

Perturbation experiments on the latent capsules of 3D Point Capsule Networks by Zhao et al.