<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://snknitin.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="https://snknitin.github.io/" rel="alternate" type="text/html" /><updated>2025-08-08T04:10:38-04:00</updated><id>https://snknitin.github.io/feed.xml</id><title type="html">Portfolio</title><subtitle>personal description</subtitle><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><entry><title type="html">Green Screen Image Composition Transfer</title><link href="https://snknitin.github.io/projects/2022/10/project-5/" rel="alternate" type="text/html" title="Green Screen Image Composition Transfer" /><published>2022-10-14T00:00:00-04:00</published><updated>2022-10-14T00:00:00-04:00</updated><id>https://snknitin.github.io/projects/2022/10/project-5</id><content type="html" xml:base="https://snknitin.github.io/projects/2022/10/project-5/"><![CDATA[<p>Using StableDiffusion for <code class="language-plaintext highlighter-rouge">text2img</code> background generation and <code class="language-plaintext highlighter-rouge">MODNET/U2net</code> model to seamlessly superimpose the isolated foreground using Alpha-matting and apply Nvidia’s <code class="language-plaintext highlighter-rouge">FastPhotoStyle</code> for an image stylization and smoothening</p>

<ul>
  <li><a href="https://huggingface.co/spaces/NikeZoldyck/green-screen-composition-transfer">Huggingface Space for Demo</a></li>
</ul>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">congratulations to <a href="https://twitter.com/Nitin_wysiwyg?ref_src=twsrc%5Etfw">@Nitin_wysiwyg</a>, bka NikeZoldyck, whose 🥞FSDL course project on Green Screen Composition is one of 🤗 <a href="https://twitter.com/huggingface?ref_src=twsrc%5Etfw">@huggingface</a>&#39;s &quot;Spaces of the Week&quot;!<br /><br />🔥 <a href="https://t.co/GPZHNxdkUW">pic.twitter.com/GPZHNxdkUW</a></p>&mdash; Full Stack Deep Learning (@full_stack_dl) <a href="https://twitter.com/full_stack_dl/status/1582505403660713984?ref_src=twsrc%5Etfw">October 18, 2022</a></blockquote>
<script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Built my first <a href="https://twitter.com/huggingface?ref_src=twsrc%5Etfw">@huggingface</a> space app as part of the <a href="https://twitter.com/full_stack_dl?ref_src=twsrc%5Etfw">@full_stack_dl</a> course project. It allows you to do 2 things : <br />1) Remove bg and superimpose on another image using alpha matting <br />2) Image Style transfer using <a href="https://twitter.com/nvidia?ref_src=twsrc%5Etfw">@nvidia</a>&#39;s FastPhotoStyle<br /><br />Check it out! <a href="https://t.co/tbkSay2uWs">https://t.co/tbkSay2uWs</a></p>&mdash; ⚡Nike_Zoldyck🔱 (@Nitin_wysiwyg) <a href="https://twitter.com/Nitin_wysiwyg/status/1580954480257814528?ref_src=twsrc%5Etfw">October 14, 2022</a></blockquote>
<script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>]]></content><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><category term="Image Stylization" /><category term="StableDiffusion" /><category term="Computer Vision" /><summary type="html"><![CDATA[Using StableDiffusion for text2img background generation and MODNET/U2net model to seamlessly superimpose the isolated foreground using Alpha-matting and apply Nvidia’s FastPhotoStyle for an image stylization and smoothening]]></summary></entry><entry><title type="html">DeepfakeCapsuleGAN</title><link href="https://snknitin.github.io/projects/2018/10/project-4/" rel="alternate" type="text/html" title="DeepfakeCapsuleGAN" /><published>2018-10-08T00:00:00-04:00</published><updated>2018-10-08T00:00:00-04:00</updated><id>https://snknitin.github.io/projects/2018/10/project-4</id><content type="html" xml:base="https://snknitin.github.io/projects/2018/10/project-4/"><![CDATA[<p>Using GANS to generate images that are then used for deep fakes. Using Capsule networks instead of regular CNNs</p>

<ul>
  <li><a href="https://github.com/snknitin/DeepfakeCapsuleGAN/blob/master/30168428_finalreport.pdf">Paper</a></li>
  <li><a href="https://github.com/snknitin/DeepfakeCapsuleGAN">Code</a></li>
</ul>]]></content><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><category term="Capsule Networks" /><category term="GANS" /><category term="DeepFake" /><category term="Computer Vision" /><category term="Generative models" /><summary type="html"><![CDATA[Using GANS to generate images that are then used for deep fakes. Using Capsule networks instead of regular CNNs]]></summary></entry><entry><title type="html">Improving Open Domain Dialogue-Systems (Chatbots)</title><link href="https://snknitin.github.io/projects/2018/04/project-3/" rel="alternate" type="text/html" title="Improving Open Domain Dialogue-Systems (Chatbots)" /><published>2018-04-08T00:00:00-04:00</published><updated>2018-04-08T00:00:00-04:00</updated><id>https://snknitin.github.io/projects/2018/04/project-3</id><content type="html" xml:base="https://snknitin.github.io/projects/2018/04/project-3/"><![CDATA[<p>Built a seq2seq neural conversational model in PyTorch using attention with intention and a diversity promoting objective function to prevent irrelevant generic outputs’</p>

<ul>
  <li><a href="http://snknitin.github.io/files/30168428_finalproject_chatbot.pdf">Paper</a></li>
  <li><a href="https://github.com/snknitin/ChatBot">Code</a></li>
</ul>]]></content><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><category term="NLP" /><category term="Seq2Seq" /><category term="Dialogue-system" /><category term="Chatbot" /><summary type="html"><![CDATA[Built a seq2seq neural conversational model in PyTorch using attention with intention and a diversity promoting objective function to prevent irrelevant generic outputs’]]></summary></entry><entry><title type="html">Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching</title><link href="https://snknitin.github.io/projects/2017/09/project-2/" rel="alternate" type="text/html" title="Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching" /><published>2017-09-01T00:00:00-04:00</published><updated>2017-09-01T00:00:00-04:00</updated><id>https://snknitin.github.io/projects/2017/09/project-2</id><content type="html" xml:base="https://snknitin.github.io/projects/2017/09/project-2/"><![CDATA[<p>Created language agnostic word embeddings via artificial code-switching to share structure across languages for any NLP task when you have less labeled data.</p>

<ul>
  <li><a href="http://snknitin.github.io/files/acs_paper.pdf">Paper</a></li>
  <li><a href="https://github.com/snknitin/Multilingual-Embeddings-using-ACS-for-Cross-lingual-NLP">Code</a></li>
</ul>]]></content><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><category term="NLP" /><category term="Embeddings" /><category term="Multi-Lingual" /><summary type="html"><![CDATA[Created language agnostic word embeddings via artificial code-switching to share structure across languages for any NLP task when you have less labeled data.]]></summary></entry><entry><title type="html">DSR Reinforcement Learning to navigate a Labyrinth</title><link href="https://snknitin.github.io/projects/2016/12/project-1/" rel="alternate" type="text/html" title="DSR Reinforcement Learning to navigate a Labyrinth" /><published>2016-12-24T00:00:00-05:00</published><updated>2016-12-24T00:00:00-05:00</updated><id>https://snknitin.github.io/projects/2016/12/project-1</id><content type="html" xml:base="https://snknitin.github.io/projects/2016/12/project-1/"><![CDATA[<p>Generalized Successor Representations from Neuroscience within an end-to-end deep reinforcement learning framework, comparing its efficacy to DQN on two diverse environments (Mazebase and DOOM) given raw pixel observations.’</p>

<ul>
  <li><a href="http://snknitin.github.io/files/DSR_paper.pdf">Paper</a></li>
  <li><a href="https://github.com/snknitin/DQN_vs_DSR-Learning-on-Grid-based-domain-and-game-engines">Code</a></li>
</ul>]]></content><author><name>Nitin Kishore Sai Samala</name><email>snk.nitin@gmail.com</email></author><category term="Reinforcement Learning" /><category term="DDQN" /><category term="RL" /><summary type="html"><![CDATA[Generalized Successor Representations from Neuroscience within an end-to-end deep reinforcement learning framework, comparing its efficacy to DQN on two diverse environments (Mazebase and DOOM) given raw pixel observations.’]]></summary></entry></feed>