visual-learning.github.io/hw2.html at master · visual-learning/visual-learning.github.io · GitHub

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                        <span class="title"> <h1>16-824: Visual Learning and Recognition</h1></span></td>
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                        <td colspan="3" align="center"><h3>Fall 2025</h3> </td>
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            <h2>Homework 2: Generative Modeling </h2>
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                <p> This homework is due on <b>11:59 PM ET Oct 22, 2025</b>. We will explore three main generative modeling architectures: GANs, VAEs, Diffusion Models, Flow Matching Models.</p>

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                <p> <b>Instructions:</b> </p>
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                    <li> Starter code and full assignment can be found <a href="https://github.com/visual-learning/generative-modeling">here</a>. </li>
                    <li> Please submit your solutions on Gradescope.</li>
                    <li> Your hand-in should consist of two parts: 1) a single PDF report and 2) all your code in a compressed zip file. Please name the report "AndrewID.pdf" and the code "AndrewID.zip". </li>
                    <li> You should follow the code structure we defined in the steps of this assignment, but small deviation is allowed. </li>
                    <li> It is okay to ask Google for help, but it is not okay to plagiarize. Feel free to search “how to add cross entropy loss in pytorch,” but do <b>not</b> copy and paste the code from online repositories. </li>
                    <li> The solution will be graded by the final result as well as the correctness of the code. You will get partial credits even if the final result is not perfect. </li>
                    <li> <b>Late Days:</b> Each student has 5 late days for the whole semester, tracked by Gradescope. Each additional late day will incur a 10% penalty on that homework.</li>
                    <li> <b>Start early!</b></li>
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                <p> <b> Acknowledgements:</b> This assignment is the result of the collective effort of all TAs that served this course over the last years. We thank all of them!</p>
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