Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. Check out the project site for all the details like and step-by-step examples. Custom distributions Community Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues. Happy brewing! License and […]

Keras Under Construction In the near future, this repository will be used once again for developing the Keras codebase. For the time being, the Keras codebase is being developed at tensorflow/tensorflow, and any PR or issue should be directed there. GitHub https://github.com/keras-team/keras    

Performer – Pytorch An implementation of Performer, a linear attention-based transformer variant with a Fast Attention Via positive Orthogonal Random features approach (FAVOR+). Install $ pip install performer-pytorch Then you must run the following, if you plan on training an autoregressive model $ pip install -r requirements.txt Usage Performer Language Model import torch from performer_pytorch import PerformerLM model = PerformerLM( num_tokens = 20000, max_seq_len = 2048, # max sequence length dim = 512, # dimension depth = 12, # layers […]

TabNet : Attentive Interpretable Tabular Learning This is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442.) https://arxiv.org/pdf/1908.07442.pdf. Easy installation You can install using pip by running:pip install pytorch-tabnet Source code If you wan to use it locally within a docker container: git clone [email protected]:dreamquark-ai/tabnet.git cd tabnet to get inside the repository CPU only make start to build and get inside the container GPU make start-gpu to build and […]

PyTorch Implementation of Differentiable ODE Solvers This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications, see reference [1]. As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU. Installation To install latest stable version: pip install torchdiffeq To install latest on GitHub: pip install git+https://github.com/rtqichen/torchdiffeq Examples […]

This repository shows two tasks: Face landmark detection and Face 3D reconstruction, which is described in this paper: Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment. Installation Clone the repository. install dependencies. pip install -r requirement.txt Running a pre-trained model Download landmark pre-trained model at GoogleDrive, and put it into FaceLandmark/model/ Run the test file python Facial_landmark.py Running a pre-trained model Download face 3D reconstruction pre-trained model at GoogleDrive, and put it into FaceReconstruction/checkpoints/ Run the […]

Token Shift GPT Implementation of Token Shift GPT – An autoregressive model that relies solely on shifting along the sequence dimension and feedforwards. Update: Inexplicably, it actually works quite well. The feedforward module follows the same design as gMLP, except the feature dimension of the gate tensor is divided up into log2(seq_len) chunks, and the mean pool of the past consecutive segments (length 1, 2, 4, 8, etc. into the past) are shifted into each chunk before a projection along […]

ActionDetection-DBG In this repo, we propose a novel and unified action detection framework, named DBG, with superior performance over the state-of-the-art action detectors BSN and BMN. You can use the code to evaluate our DBG for action proposal generation or action detection. For more details, please refer to our paper Fast Learning of Temporal Action Proposal via Dense Boundary Generator! Paper Introduction This paper introduces a novel and unified temporal action proposal generator named Dense Boundary Generator (DBG). In this […]

CPT Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accepted at ICLR 2021 (Spotlight) [Paper Link]. Overview Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs’ training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs’ precision might have a similar […]

lightweight-gan 512×512 flowers after 12 hours of training, 1 gpu 256×256 flowers after 12 hours of training, 1 gpu Pizza ‘Lightweight’ GAN Implementation of ‘lightweight’ GAN proposed in ICLR 2021, in Pytorch. The main contributions of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary “converge on single gpu with few hours’ training, on 1024 resolution sub-hundred images”. Install $ pip install lightweight-gan Use One command $ lightweight_gan […]