Neural Information Processing Systems http://nips.cc/

In the case of, e.g., object classification in images collected by

Adversarial attacks in the input (pixel) space typically incorporate noise margins such as $L_1$ or $L_{\infty}$-norm to produce imperceptibly perturbed data that confound deep learning networks. Such noise margins confine the magnitude of permissible noise. In this work, we propose injecting adversarial perturbations in the latent (feature) space using a generative adversarial network, removing the need for margin-based priors. Experiments on MNIST, CIFAR10, Fashion-MNIST, CIFAR100 and Stanford Dogs datasets support the effectiveness of the proposed method in generating adversarial attacks in the latent space while ensuring a high degree of visual realism with respect to pixel-based adversarial attack methods.

Lately some high-profile tools have emerged that can help in programming like Github Copilot and ChatGPT. Is the programming job market going to shrink because of this? I think I will be able to find a programming job even in the year 2050, but before I reveal why, I'll lead with some exploration of ChatGPT. I'll start with an experiment. I'll ask ChatGPT directly whether it will replace programmers, and then will ask it to program a neural network for me.

The ResNet architecture is considered to be among the most popular Convolutional Neural Network architectures around. Introduced by Microsoft Research in 2015, Residual Networks (ResNet in short) broke several records when it was first introduced in this paper by He. et. The requirement for a model like ResNet arose due to a number of pitfalls in modern networks at the time. Adding layers can be seen as an expansion of the function space. For example, multiple layers added together can be seen as a function F. This function F can be expressed as a representation of a function space F that it can reach/model.

In this project, we develop an algorithm that takes in an image and identifies if contains a dog or a human. If it does contain either a dog or a human, the algorithm will classify the dog's breed or the dog breed that closely resembles that human. This problem falls under the popular category of computer vision. To solve these problems we will be using machine learning methods. We will look at a couple of different methods for each problem and identify which is better. We will also focus on the dog breed classifier and use off-the-shelf tools for the human and dog detectors.

Let's start with the 3rd Project -- Stanford Dog Dataset. This dataset asks you to identify dogs of 120 different breeds. We can go with our previous approach. But that will take a lot of computation and a lot of time. Let's introduce a new concept then.