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A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Implemented 3 neural network architectures: 1) Combination of RNN LSTM nodes and CNN, 2) CNN with residual blocks similar to ResNet, 3) Deep RNN LSTM network; and compared their performance to detect 12 speech commands.
Weed Detection in Sugar Beet Plants
Graduation Project. Applying Generative Adversarial Networks(GAN) with Residual-In-Residual(RIR) blocks.
This is an implementation of FRED-Net using keras.
Semantic segmentation for brain tumors
Deep learning model to predict the normal flow between two consecutive frames, being the normal flow the projection of the optical flow on the gradient directions.
Fast bare-bones implementation of convolutional layers, residual blocks, Adam optimizer, backpropagation and custom accuracy and loss functions (F1 score per pixel and binary-crossentropy)