Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam:Optimizing the Latent Space of Generative Networks I’m not one of the authors. I just reimplemented parts of the paper in PyTorch for learning about PyTorch and generative models. Also, I liked the idea in the paper and was surprised that the approach actually works. Implementation of the Laplacian pyramid L1 loss is inspired by https://github.com/mtyka/laploss. DCGAN network architecture […]

Poincaré Embeddings for Learning Hierarchical Representations PyTorch implementation of Poincaré Embeddings for Learning Hierarchical Representations Installation Simply clone this repository via git clone https://github.com/facebookresearch/poincare-embeddings.git cd poincare-embeddings conda env create -f environment.yml source activate poincare python setup.py build_ext –inplace Example: Embedding WordNet Mammals To embed the transitive closure of the WordNet mammals subtree, first generate the data via cd wordnet python transitive_closure.py This will generate the transitive closure of the full noun hierarchy as well as of the mammals subtree of […]

adversarial-patch PyTorch implementation of adversarial patch This is an implementation of the Adversarial Patch paper. Not official and likely to have bugs/errors. How to run: Data set-up: Run attack: python make_patch.py –cuda –netClassifier inceptionv3 –max_count 500 –image_size 299 –patch_type circle –outf log Results: Using patch shapes of both circles and squares gave good results (both achieved 100% success on the training set and eventually > 90% success on test set) I managed to recreate the toaster example in the original […]

PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. ENAS reduce the computational requirement (GPU-hours) of Neural Architecture Search (NAS) by 1000x via parameter sharing between models that are subgraphs within a large computational graph. SOTA on Penn Treebank language modeling. Prerequisites Python 3.6+ PyTorch==0.3.1 tqdm, scipy, imageio, graphviz, tensorboardX Usage Install prerequisites with: conda install graphviz pip install -r requirements.txt To train ENAS to discover a recurrent cell for RNN: python main.py –network_type rnn –dataset ptb –controller_optim adam […]