MLiP

This repository contains code and supplementary material for participated Kaggle Challenges.

Challenge 1: I'm Something of a Painter Myself

The first challenge can be found on Kaggle: https://www.kaggle.com/c/gan-getting-started. This challenge includes style transfer from Monet style to the 'COCO2017' dataset (https://www.tensorflow.org/datasets/catalog/coco). As a result, we created 7000 images in the style of Monet.

We are inspired by the model introduced by Johnson et al. [1]. This model introduces neural style transfer with the use of a pre-trained VGG-16 network.

The code and results of this challenge can be found in the corresponding folder: Challenge1 - I'm Something of a Painter myself. This folder exists of the following files:

• johnson-v2.ipynb; A python notebook containing the source code
• johnson-v2.zip; A zipped version of the notebook (as it is quite large)
• model.zip; The final model created for neural style transfer
• graphs.zip; Graphs with various model outcomes (loss, norm of weights, mean gradient and variance of gradient)
• johnson_v2_best.ipynb; The notebook which gave the highest score in the challenge (but we don't use this as our final submission).
• johnson-v2_best.zip; A zipped version of the notebook (as it is quite large)

[1]Johnson, Justin, e.a. ‘Perceptual Losses for Real-Time Style Transfer and Super-Resolution’. arXiv:1603.08155 [cs], maart 2016. arXiv.org, http://arxiv.org/abs/1603.08155.

Challenge 2: Coleridge Initiative - Show US the Data

The second challenge can be found on Kaggle: https://www.kaggle.com/c/coleridgeinitiative-show-us-the-data.
This challenge includes the recognition of public dataset in scientifc papers.

The code and results of this challenge can be found in the corresponding folder: Challenge2 - Coleridge Initiative - Show US the Data. This folder exists of the following files:

• Part I_Creating_Dataset.ipynb; A python notebook containing the source code for creating the dataset for training.
• Part_IIa_BERT_Training.ipynb; A python notebook containing the source code for training the BERT model.
• Part_IIb_SciBERT_Training.ipynb; A python notebook containing the source code for training the SciBERT model.
• Part III_With_LiteralMatching_SciBERT.ipynb; A python notebook containing the source code for predicting labels on the test set using the SciBERT model with literal matching (and final submission)
• Part III_Without_LiteralMatching_SciBERT.ipynb; A python notebook containing the source code for predicting labels on the test set using the SciBERT model without literal matching
• Part III_With_LiteralMatching_BERT.ipynb; A python notebook containing the source code for predicting labels on the test set using the SciBERT model with literal matching
• Part III_Without_LiteralMatching_BERT.ipynb; A python notebook containing the source code for predicting labels on the test set using the SciBERT model without literal matching
• trianingResults.json; the trainnig loss and learning rate of the training process of both BERT and SciBERT
• trainingProcess.py; A python script to create visualization for the training loss and learning rate of BERT and SciBERT.
• Figures/ ; The outcomes of trainingProcess.py