Time Delayed NN implemented in PyTorch. 
kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)]
tdnn = TDNN(kernels, input_embedding_size)
# in is tensor with shape [batch_size, max_seq_len, max_word_len, input_embedding_size]
out = tdnn(in)
SLATE This is the official source code for SLATE. We provide the code for the model, the training code and a dataset loader for the 3D Shapes dataset.
Equivariant CNNs for the sphere and SO(3) implemented in PyTorch
AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-
Unet this is an implemetion of Unet in Pytorch and it's architecture is as follows which is the same with paper of Unet component of Unet Convolution
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Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories. Metrics provides i
Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the output of YOLOv4 feed these object detections into Deep SORT (Simple Online and Realtime Tracking with a Deep Association Metric) in order to create a highly accurate object tracker.
Efficient implementation of YOLOV5 in TensorFlow2
excuse me,I have a question. kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)]
what are the(1,25),(2,50)....... mean? for example,I have a speech dateset which has 10000 frames * 2576 features. 2576 fratures per frame.Input is 1*2576. I want to implement speech separation by tdnn,batch_size is 200,target is IRM(1 * 161). what is "kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)] "mean? And what can I get if I use it as my hidden layer? Thank you very much,I need help to complete my graduation project.And My english is poor, please forgive me.I appreciate your reply.Thanks a lot.
PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt
SimDeblur (Simple Deblurring) is an open source framework for image and video deblurring toolbox based on PyTorch, which contains most deep-learning based state-of-the-art deblurring algorithms. It is easy to implement your own image or video deblurring or other restoration algorithms.
Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话）. Many modifications t
Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici
transformer-pytorch Transformer model implemented with Pytorch Attention is all you need-[Paper] Architecture Self-Attention self_attention.py class
Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Includes smoo
Pytorch Negative Sampling Loss Negative Sampling Loss implemented in PyTorch. Usage neg_loss = NEG_loss(num_classes, embedding_size) optimizer =
Pytorch Recurrent Variational Autoencoder Model: This is the implementation of Samuel Bowman's Generating Sentences from a Continuous Space with Kim's
PyTorch Highway Networks Highway networks implemented in PyTorch. Just the MNIST example from PyTorch hacked to work with Highway layers. Todo Make th
Reinforcement learning framework and algorithms implemented in PyTorch.
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