Publishing a paper in CVPR

vishnu_vardhan1: <p>Hi<br>
After reading and going through few of the groundbreaking CNN architectures, I started</p>
<blockquote>
<p>creating My own architecture in PyTorch and tested on Cats and Dogs dataset</p>
</blockquote>
<p>having 25,000 images, which is open sourced by Microsoft for research purposes.</p>
<p>I have split my data into train, valid and 2000 images for testing( predictions to check the model accuracy).</p>
<p>And I have got</p>
<blockquote>
<p>96.32% accuracy on test data by my architecture which I build from scratch</p>
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<p>. Now, can anyone please tell me whether I can publish my model architecture in CVPR? If yes, please tell me what are the steps to follow?</p>

ptrblck: <p>I would recommend to try out your architecture on a broader set of datasets (e.g. ImageNet) and check the performance, since the mentioned dataset might be too small and limited to claim a superior general model architecture.</p>

vishnu_vardhan1: <p>Hi <a class="mention" href="/u/ptrblck">@ptrblck</a>, that was my first thought. But to train the datasets such as Imagenet and JFT, I neither have Such kind of storage nor computing power to process, or even the money to pay for resources for AWS or GCP.</p>
<p>I’m an undergrad student who’s staying at home due to lockdown and using Google colab for my work. I cannot afford AWS or GCP.</p>
<p>If you have any suggestions please help me!</p>

ptrblck: <p>That’s understandable.<br>
If you don’t have the computing resources, you could try to take a look at the model analytically and perform some experiments in this direction. E.g. the <a href="https://arxiv.org/abs/1710.09829">Capsule Network paper</a> used MNIST, CIFAR10, smallNORB, and SVHN for their initial experiments, but added a theoretical reasoning why this architecture works.<br>
Maybe you could try a similar approach.</p>

vdw: <p>Just to add to <a class="mention" href="/u/ptrblck">@ptrblck</a> comments – coming from a university research perspective, although not in image/video processing.</p>
<p>Deep Learning is both data and resource-hungry. This is why on many top papers, at least one author is from a company like Microsoft, Facebook, etc. That means, being smart brings you in many cases only so far. AI is also a very crowded field and CVPR is extremely(!) difficult to get into. And the works that do come from very strong groups that have also the required resources.</p>
<p>I’m really not trying to question your capabilities, but you might want to bit more pragmatic with your goals. For example, the paper that <a class="mention" href="/u/ptrblck">@ptrblck</a> links is from the Hinton group. And Hinton won the Turing Award for his work in Deep Learning.</p>
<p>By they way according to my image/video colleagues, a “cat vs. dog” classifier should do around 99% at least these days :).</p>

vishnu_vardhan1: <p><a class="mention" href="/u/vdw">@vdw</a> you are very much correct, and you did not question my capabilities but gave me an intuition on how the people are in this respective field.</p>
<p>Thank you very much <img src="https://discuss.pytorch.org/images/emoji/apple/slightly_smiling_face.png?v=9" title=":slightly_smiling_face:" class="emoji" alt=":slightly_smiling_face:"></p>