The thesis can be downloaded in PDF from here: https://dspace.cuni.cz/handle/20.500.11956/173547
- prepare this repository for submission
- fill out xmpdata files
- prepare abstract in the thesis and in the abstract.pdf file
An Auto-Encoder Strategy for Adaptive Image Segmentation: https://arxiv.org/pdf/2004.13903.pdf
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling: https://arxiv.org/pdf/1806.11266.pdf
An Overview of Deep Semi-Supervised Learning https://arxiv.org/pdf/2006.05278.pdf
Few-Shot semantic segmentation papers https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers
Stacked Convolutional Sparse Auto-Encoders for Representation Learning https://dl.acm.org/doi/abs/10.1145/3434767
Sparse autoencoder: Lecture notes (Andrew Ng) https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf
Awesome list: https://github.com/yassouali/awesome-semi-supervised-learning
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks https://arxiv.org/pdf/1701.04128.pdf
Dynamic Routing Between Capsules https://cmp.felk.cvut.cz/~toliageo/rg/papers/SabourFrosstHinton_NIPS2017_Dynamic%20Routing%20Between%20Capsules.pdf
Original Dropout paper: https://jmlr.org/papers/volume15/srivastava14a.old/srivastava14a.pdf
Dropout on convultional layers may not be ideal (3.2): https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Tompson_Efficient_Object_Localization_2015_CVPR_paper.pdf
Dying ReLU problem: https://arxiv.org/pdf/1903.06733.pdf
Autoencoder(s), overview of all: https://arxiv.org/pdf/2003.05991.pdf
DeepScores v1: https://arxiv.org/pdf/1804.00525.pdf
DeepScores v2: https://ieeexplore.ieee.org/document/9412290/
Variational autoencoder: https://arxiv.org/abs/1312.6114
Semisup pytorch github: https://github.com/wohlert/semi-supervised-pytorch
Adam (Kingma et al): https://arxiv.org/abs/1412.6980
Object detection metrics: https://github.com/rafaelpadilla/Object-Detection-Metrics
Cuneiforms: https://link.springer.com/chapter/10.1007/978-3-030-86549-8_5
Stacked denoising autoencoders: https://arxiv.org/pdf/1606.08921.pdf