Created and compared the performance of three different models for patent application classification. The first two models use different word representations, while the third is based on a pre-trained language model.
Accuracy improved significantly—from 60% with the baseline model (using GloVe embeddings) to 88% with the pre-trained model.
Programming language: Python 3.7
Word representation: GloVe word embeddings, pre-trained domain-specific embeddings
Pre-trained language model: Google AI's BERT
Python’s libraries: Pandas, Numpy
Deep Learning framework: Tensorflow
Passau, Germany
Master’s thesis at the University of Passau
University of Passau
Depth information is extracted from RGB images using Generative Adversarial Networks (GANs). The generator follows an encoder-decoder architecture. A pre-trained EfficientNet model is used as the encoder backbone for feature extraction, while the decoder is implemented as a fully convolutional neural network.
In the first approach, Conditional GANs (CGANs) are trained in a supervised manner using single RGB images paired with corresponding ground truth depth maps for both training and testing.
The second approach employs a GAN-based architecture trained in an unsupervised fashion using stereo image pairs without ground truth depth maps; at test time only single RGB images with their corresponding depth maps are used.