[mlpack] GSoC 2020 Ideas

[Andrei M]

Hello again,

Thank you for the feedback.

After a longer though process, I decided I would like to implement the
DeepLabv3+ model for semantic segmentation as part of the ANN models
project. This implies several phases of implementation and this is the
split I propose:

Step 1:
Implement a dataloader for a semantic segmentation dataset: This will be
either Pascal VOC 2012 or ADE20K.

Step 2:
Implement an Xception model backbone, with atrous depth-wise separable
convolutions. This is the backbone that makes the model yield the best
performance, according to the original paper, overpassing the ResNet-101
backbone.

Step 3:
a. Implement the encoder architecture of the model, which is a DeepLabv3,
that uses the previously mentioned Xception backbone. This task also
implies the building of the atrous spatial pyramid pooling module.
b. Implement the decoder architecture, which is a simple architecture based
on convolutions, which refine the segmentation results of the encoder

Step 4:
Train and test the implemented model on the selected dataset, then compare
the results with the ones obtained in the paper. Visualize the results and
create relevant plots and statistics.

That would be a shorter version of my proposal.

Best,
Andrei

On Wed, 11 Mar 2020 at 23:35, Marcus Edel <marcus.edel at fu-berlin.de> wrote:

> Hello Andrei,
>
> 1. RL: I've taken a more in-depth look on the reinforcement learning
> module. The
> DQN, Double DQN and prioritized replay are already implemented, so as part
> of
> the rainbow the remaining components are Dueling networks, Multi-step
> learning,
> Distributional RL, Noisy. Therefore, I suggest finishing the
> implementation of
> the Rainbow DQN and then an implementation of the ACKTR algorithm.
>
> Sounds totally reasonable to me.
>
> 2. Applications of ANN: Implementing a U-Net or DeepLabv3 architecture for
> semantic segmentation.
>
> I like both models, also good that you mentioned you like to focus on
> either
> U-Net or DeepLabv3.
>
> I would like to know if the ideas above would make enough for a summer
> project
> for each of the two sections.
>
> Definitely, a big part of each project is documentation and testing,
> writing
> good tests takes time.
>
> Let me know if I should clarify anything further.
>
> Thanks,
> Marcus
>
> On 10. Mar 2020, at 15:50, Andrei M <mihalea.andrei at gmail.com> wrote:
>
> Hello,
>
> Thank you for the response.
>
> I've been thinking more about the ideas for the GSoC and I've established
> a top 2 I'd like to work on: Reinforcement learning or Applications of ANN.
> (I'll only select one for the final proposal)
>
> 1. RL: I've taken a more in-depth look on the reinforcement learning
> module. The DQN, Double DQN and prioritized replay are already implemented,
> so as part of the rainbow the remaining components are Dueling networks,
> Multi-step learning, Distributional RL, Noisy. Therefore, I suggest
> finishing the implementation of the Rainbow DQN and then an implementation
> of the ACKTR algorithm.
>
> 2. Applications of ANN: Implementing a U-Net or DeepLabv3 architecture for
> semantic segmentation.
>
> I would like to know if the ideas above would make enough for a summer
> project for each of the two sections.
>
> Thank you,
> Andrei
>
> On Mon, Mar 9, 2020 at 1:22 AM Marcus Edel <marcus.edel at fu-berlin.de>
> wrote:
>
>> Hello Andrei,
>>
>> welcome and thanks for you interest. Looks like you already brainstormed
>> about
>> the ideas, that great. I think each method you proposed made sense, there
>> is
>> alrady a PR open for PPO (https://github.com/mlpack/mlpack/pull/1912)
>> which is
>> very close to being merged, so I think you can remove that from the list.
>>
>> Also, I think both ideas could be combined, like if you add a new layer
>> to the
>> codebase. That said, we don't have project priorities, so you a free to
>> go with
>> anything you find interesting.
>>
>> Let me know if I should clarify anything.
>>
>> Thanks,
>> Marcus
>>
>> On 6. Mar 2020, at 15:47, Andrei M <mihalea.andrei at gmail.com> wrote:
>>
>> Hello,
>>
>> I'm a second year master's degree student in the field of artificial
>> intelligence and I've been thinking about applying to Google Summer of Code
>> for this summer and mlpack is the project I want to work on.
>>
>> I've spent the last few weeks to get familiar with the code base and
>> write some code for a new feature (a loss function that wasn't
>> implemented). There are several ideas in the list that peaked my interest
>> and I consider them equally interesting: reinforcement learning, essential
>> deep learning modules, application of ANN algorithms implemented in mlpack
>> and improvisation and implementation of ANN modules.
>>
>> I think these ideas would fit well for me since I've been implementing
>> neural networks such as DQN, Double DQN, Dueling networks, GANS and several
>> others in PyTorch and I've also been in touch with the state-of-art
>> research in various fields, like the ones mentioned above. Therefore, I
>> think I would equally enjoy working on the reinforcement learning path and
>> working on bringing features and modules that are present in other
>> libraries, like PyTorch.
>>
>> Below are some summaries of the ideas I'm thinking about:
>> *Reinforcement learning*: Here I would like to work on Rainbow and
>> Proximal Policy Optimization Algorithms, train and test them on different
>> environments and empirically show their advantages and disadvantages (for
>> example how double DQN can reduce the overestimation problem that appears
>> in DQN).
>> *Application of ANN algorithms implemented in mlpack*: For this idea, I
>> have two options that come to my mind: first one is implementing a sequence
>> to sequence model for language translation and the other consists of
>> implementing U-Net like architectures which are usually employed for
>> segmentation tasks or depth prediction.
>> *Essential deep learning modules*: The plan I propose for this idea is
>> implementing some of the GAN architectures that aren't yet implemented,
>> starting from the first types of GANs that appeared, like conditional GANs
>> and info GANs, then advancing to more modern ones, trying to obtain and
>> visualize the results shown in the papers they've been presented on.
>>
>> I would also like to know what are the features with high priority for
>> mlpack to have and if you have any suggestion on what I should propose to
>> match these priorities.
>>
>> Also, can more ideas be proposed in a single application?
>> Any feedback and suggestions are appreciated.
>>
>> Best,
>> Andrei
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>>
>>
>>
>
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