QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently build and visualize their training data set. Introduction QuakeLabeler is a Python package to customize, build and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing. Current functionalities include retrieving waveforms from data centers, customizing seismic samples, auto-building datasets, preprocessing and augmenting for labels, […]

English | 简体中文 Why Non-Euclidean Geometry Considering these simple graph structures shown below. Nodes with same color has 2-hop distance whereas 1-hop distance between nodes with different color. Now how could we embed these structures in Euclidean space while keeping these distance unchanged? Actually perfect embedding without distortion, appearing naturally in hyperbolic (negative curvature) or spherical (positive curvature) space, is infeasible in Euclidean space [1]. As shown above, due to the high capacity of modeling complex structured data, e.g. scale-free, […]

StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper “Deep Stable Learning for Out-Of-Distribution Generalization” and the arXiv version can be found at https://arxiv.org/abs/2104.07876. Introduction Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume […]

The (Official) PyTorch Implementation of the paper “Deep Extraction of Manga Structural Lines” Usage model_torch.py [source folder] [output folder] Example: model_torch.py ./pytorchTestCases/ ./pytorchResults/ The model weights (erika.pth) Please refer to the release section of this repo. Alternatively, you may use this link: https://www.dropbox.com/s/y8pulix3zs73y62/erika.pth?dl=0 Requirement Python3 PyTorch (tested on version 1.9) Python-opencv How the model is prepared The PyTorch weights are exactly the same as the theano(!) model. I make some efforts to convert the original weights to the new model […]

GeDML GeDML is an easy-to-use generalized deep metric learning library, which contains: State-of-the-art DML algorithms: We contrain 18+ losses functions and 6+ sampling strategies, and divide these algorithms into three categories (i.e., collectors, selectors, and losses). Bridge bewteen DML and SSL: We attempt to bridge the gap between deep metric learning and self-supervised learning through specially designed modules, such as collectors. Auxiliary modules to assist in building: We also encapsulates the upper interface for users to start programs quickly and […]

DeepConsensus uses gap-aware sequence transformers to correct errors in PacificBiosciences (PacBio) Circular Consensus Sequencing (CCS) data. Installation From pip package pip install deepconsensus==0.1.0 You can ignore errors regarding google-nucleus installation, such as ERROR: Failed building wheel for google-nucleus. From source git clone https://github.com/google/deepconsensus.git cd deepconsensus source install.sh (Optional) After source install.sh, if you want to run all unit tests, you cando: ./run_all_tests.sh Usage See the quick start. Where does DeepConsensus fit into my pipeline? After a PacBio sequencing run, DeepConsensus […]

Quick Start: The easiest way to colorize images using DeOldify (for free!) is here: DeOldify Image Colorization on DeepAI The most advanced version of DeOldify image colorization is available here, exclusively.  Try a few images for free! MyHeritage In Color Image (artistic) |Video NEW Having trouble with the default image colorizer, aka “artistic”?  Try the “stable” one below.  It generally won’t produce colors that are as interesting as “artistic”, but the glitches are noticeably reduced. Image (stable) Instructions on how […]

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. TFLearn features include: Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics… Full transparency over Tensorflow. All functions are built over tensors and can […]

praudio praudio provides objects and a script for performing complex preprocessing operations on entire audio datasets with one command. praudio is implemented having Deep Learning audio/music applications in mind. Operations are carried out on CPU. Preprocessing can also be run on-the-fly, for example, while training a model. The library uses librosa as an audio processing backend. How do I install the library? You can install praudio both with pip via PyPi, and by cloning the praudio repo from GitHub. For […]