tejus-gupta

\begin{figure}[t]
\begin{center}
\includegraphics[width=1in,height=1in]{images/flute.png}
\hspace{0.1cm}
\includegraphics[width=1in,height=1in]{pictures/adv12.png}
\hspace{0.1cm}
\includegraphics[width=1in,height=1in]{images/carpenter_kit.png}
\end{center}

\begin{center}

tejus-gupta

\begin{figure*}
\begin{center}
\includegraphics[width=3.132in,height=2.349in]{Figure_1-11.png}
\hspace{0.2cm}
\includegraphics[width=3.132in,height=2.349in]{Figure_1-6.png}
\end{center}


\begin{center}
    \caption{A. (left) Ratio of $i^{th}$ singular value to first singular value of matrix $P$ containing example-wise adversarial perturbations. B. (right) Cosine similarity of our universal perturbation for class '0' with singular vectors of matrix $P$.}

tejus-gupta

\begin{figure*}[t]
\begin{center}
\includegraphics[width=1.4in,height=1.4in]{pictures/vgg16_envelope.png}
\hspace{0.2cm}
\includegraphics[width=1.4in,height=1.4in]{pictures/vgg16_horsecart.png}
\hspace{0.2cm}
\includegraphics[width=1.4in,height=1.4in]{pictures/vgg16_tablelamp.png}
\end{center}

\begin{center}

tejus-gupta

mkdir train_set
cd train_set
wget https://gist.githubusercontent.com/tejus-gupta/3d4564e624cad79691706a5c1303f4c6/raw/3cafe4877f981e3f3c481727d0a90db519a4e95b/download.py
python download.py
unzip -qq masks.zip
unzip -qq train_data.zip
cd ..
git clone https://github.com/tejus-gupta/Segmentation
cd Segmentation
git checkout modelD

tejus-gupta

import os
import sys
import yaml
import time
import shutil
import torch
import random
import argparse
import datetime
import numpy as np

tejus-gupta

import os
import sys
import yaml
import time
import shutil
import torch
import random
import argparse
import datetime
import numpy as np

tejus-gupta

to_remove = []

for class_idx in selected_classes:
	if np.sum(y_train[:, class_idx] < 0.5) < 5 or np.sum(y_train[:, class_idx] > 0.5) < 5:
		to_remove.append(class_idx)

for class_idx in to_remove:
	selected_classes.remove(class_idx)

print('Removed classes with too few examples')

tejus-gupta

<launch>
  <!-- 
  NOTE: You'll need to bring up something that publishes sensor data (see
  rosstage), something that publishes a map (see map_server), and something to
  visualize a costmap (see nav_view), to see things work.

  Also, on a real robot, you'd want to set the "use_sim_time" parameter to false, or just not set it.
  -->
  <param name="/use_sim_time" value="true"/>

tejus-gupta

    if message_type == 'user_read':
        key,_ = data.split('|')

        highest_version = -1
        highest_version_value = ''

        #nodes = random.sample([(hash(key)+i)%N for i in range(R)], Q_r)
        #nodes = random.sample(get_next_live_inc(hash(key), R), Q_r)

        nodes = get_next_live_inc(hash(key), R)

tejus-gupta

Project Abstract

I proposed to

1. Add a package for image segmentation as part of JuliaImages with several algorithms -

• Thresholding - Otsu’s method and Adaptive thresholding
• K-means clustering
• Mean shift segmentation
• Watershed segmentation
• Felzenszwalb's efficient region merging algorithm
• Shi and Malik’s normalized graph-cut based segmentation