DeepMetaHandles is a shape deformation technique. It learns a set of meta-handles for each given shape. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation direction. A new deformation can then be generated by the “linear combination” of the meta-handles. Although the approach is learned in an unsupervised manner, the learned meta-handles possess strong interpretability and consistency. Environment setup Create a conda environment by conda env create -f environment.yml. […]

DA2Lite DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. Install git clone https://github.com/da2so/DA2Lite.git You will need a machine with a GPU and CUDA installed.Then, you prepare runtime environment: pip install -r requirements.txt Use Run main.py(DA2Lite) runs with two main configurations like as follows: CUDA_VISIBLE_DEVICES=0 python main.py –train_config_file=./configs/train/cifar10/cifar10/vgg16.yaml –compress_config_file=./configs/compress/tucker.yaml The first one is train_config_file, which indicates training configurations and the other is compress_config_file, which represents compress configurations.The details of available configurations are described in […]

torchsynth torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-optional and differentiable. Most synthesizers are fast in terms of latency. torchsynth is fast in terms of throughput. It synthesizes audio 16200x faster than realtime (714MHz) on a single GPU. This is of particular interest to audio ML researchers seeking large training corpora. Additionally, all synthesized audio is returned with the underlying latent parameters used for generating the corresponding audio. This is useful for multi-modal training regimes. […]