Hello, Thank you for the feedback. I submitted a draft proposal based on it.
Best, Andrei On Sat, Mar 21, 2020, 00:43 Marcus Edel <[email protected]> wrote: > 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]> > 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]> 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]> >> 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 <[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] >>> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >>> >>> >>> >> >
_______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
