Hello Andrei, thanks for the update, I don't have anything to add, sounds totally reasonable to me. As an overall timeline, this could definitely work.
Thanks, Marcus > On 19. Mar 2020, at 13:07, Andrei M <[email protected]> wrote: > > 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 <[email protected] > <mailto:[email protected]>> 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 <[email protected] >> <mailto:[email protected]>> 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 <[email protected] >> <mailto:[email protected]>> 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 >> <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 <[email protected] >>> <mailto:[email protected]>> 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 >>> _______________________________________________ >>> mlpack mailing list >>> [email protected] <mailto:[email protected]> >>> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >>> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >> >
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