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adversarial-attacks-pytorch

This repository contains implementation of 4 adversarial attacks : FGSM, Basic Iterative Method, Projected Gradient Descent(Madry's Attack), and Carlini Wagner's L2 attack. Also contained is the code to visualise it, along with a detailed report and a poster explaining the various attacks.

anishmadan_xyz.github.io

A beautiful, simple, clean, and responsive Jekyll theme for academics

b-small

This repo contains code for the paper: "B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning".

c3vqg-official

This repository contains code for the paper "C3VQG: Category Consistent Cyclic Visual Question Generation".

chain-reaction

cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

cmu-vision.github.io

cocoapi-lt3d

deep3d-pytorch

deeplearning.ai-summary

This repository contains my personal notes and summaries on DeepLearning.ai course. I've enjoyed every little bit of the course hope you enjoy my notes too.

detectron2-lt3d

foundational_fsod

This repository contains the implementation for the paper "Revisiting Few Shot Object Detection with Vision-Language Models"

glow_normalizing_flow

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

make-a-pr

maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

maml_pytorch_rl

mca_assignments

mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

nerfies

This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.

ners

Code for "NeRS: Neural Reflectance Surfaces for Sparse-View 3D Reconstruction in the Wild," in NeurIPS 2021

nuscenes-download

script for downloading nuscenes

recipes

semantic-segmentation-indian-driving-dataset

This repository implements Semantic Segmentation of the Indian Driving Dataset. It contains two architectures to achieve this: FCN8s and SegNet.

tutorial