paper links

Author: vpj

labml_nn/capsule_networks/__init__.py

@@ -10,7 +10,7 @@
 # Capsule Networks
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of
-[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829).
+[Dynamic Routing Between Capsules](https://papers.labml.ai/paper/1710.09829).
 
 Capsule network is a neural network architecture that embeds features
 as capsules and routes them with a voting mechanism to next layer of capsules.

labml_nn/capsule_networks/mnist.py

@@ -9,7 +9,7 @@
 This is an annotated PyTorch code to classify MNIST digits with PyTorch.
 
 This paper implements the experiment described in paper
-[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829).
+[Dynamic Routing Between Capsules](https://papers.labml.ai/paper/1710.09829).
 """
 from typing import Any
 

labml_nn/capsule_networks/readme.md

@@ -1,7 +1,7 @@
 # [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of
-[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829).
+[Dynamic Routing Between Capsules](https://papers.labml.ai/paper/1710.09829).
 
 Capsule network is a neural network architecture that embeds features
 as capsules and routes them with a voting mechanism to next layer of capsules.

labml_nn/gan/cycle_gan/__init__.py

@@ -9,7 +9,7 @@
 # Cycle GAN
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
-[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593).
+[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://papers.labml.ai/paper/1703.10593).
 
 I've taken pieces of code from [eriklindernoren/PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN).
 It is a very good resource if you want to checkout other GAN variations too.

labml_nn/gan/cycle_gan/readme.md

@@ -1,4 +1,4 @@
 # [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
 
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
-[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593).
+[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://papers.labml.ai/paper/1703.10593).

labml_nn/gan/dcgan/__init__.py

@@ -7,7 +7,7 @@
 # Deep Convolutional Generative Adversarial Networks (DCGAN)
 
 This is a [PyTorch](https://pytorch.org) implementation of paper
-[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434).
+[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://papers.labml.ai/paper/1511.06434).
 
 This implementation is based on the [PyTorch DCGAN Tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html).
 """

labml_nn/gan/dcgan/readme.md

@@ -1,4 +1,4 @@
 # [Deep Convolutional Generative Adversarial Networks - DCGAN](https://nn.labml.ai/gan/dcgan/index.html)
 
 This is a [PyTorch](https://pytorch.org) implementation of paper
-[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434).
+[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://papers.labml.ai/paper/1511.06434).

labml_nn/gan/original/__init__.py

@@ -7,7 +7,7 @@
 # Generative Adversarial Networks (GAN)
 
 This is an implementation of
-[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
+[Generative Adversarial Networks](https://papers.labml.ai/paper/1406.2661).
 
 The generator, $G(\pmb{z}; \theta_g)$ generates samples that match the
 distribution of data, while the discriminator, $D(\pmb{x}; \theta_g)$

labml_nn/gan/original/readme.md

@@ -1,4 +1,4 @@
 # [Generative Adversarial Networks - GAN](https://nn.labml.ai/gan/original/index.html)
 
 This is an annotated implementation of
-[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
+[Generative Adversarial Networks](https://papers.labml.ai/paper/1406.2661).

labml_nn/gan/stylegan/__init__.py

@@ -8,12 +8,12 @@
 # StyleGAN 2
 
 This is a [PyTorch](https://pytorch.org) implementation of the paper
- [Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/abs/1912.04958)
+ [Analyzing and Improving the Image Quality of StyleGAN](https://papers.labml.ai/paper/1912.04958)
  which introduces **StyleGAN 2**.
 StyleGAN 2 is an improvement over **StyleGAN** from the paper
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948).
+ [A Style-Based Generator Architecture for Generative Adversarial Networks](https://papers.labml.ai/paper/1812.04948).
 And StyleGAN is based on **Progressive GAN** from the paper
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196).
+ [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://papers.labml.ai/paper/1710.10196).
 All three papers are from the same authors from [NVIDIA AI](https://twitter.com/NVIDIAAI).
 
 *Our implementation is a minimalistic StyleGAN 2 model training code.
@@ -650,7 +650,7 @@ class DownSample(nn.Module):
     The down-sample operation [smoothens](#smooth) each feature channel and
      scale $2 \times$ using bilinear interpolation.
     This is based on the paper
-     [Making Convolutional Networks Shift-Invariant Again](https://arxiv.org/abs/1904.11486).
+     [Making Convolutional Networks Shift-Invariant Again](https://papers.labml.ai/paper/1904.11486).
     """
 
     def __init__(self):
@@ -672,7 +672,7 @@ class UpSample(nn.Module):
 
     The up-sample operation scales the image up by $2 \times$ and [smoothens](#smooth) each feature channel.
     This is based on the paper
-     [Making Convolutional Networks Shift-Invariant Again](https://arxiv.org/abs/1904.11486).
+     [Making Convolutional Networks Shift-Invariant Again](https://papers.labml.ai/paper/1904.11486).
     """
 
     def __init__(self):
@@ -824,7 +824,7 @@ class GradientPenalty(nn.Module):
     ## Gradient Penalty
 
     This is the $R_1$ regularization penality from the paper
-    [Which Training Methods for GANs do actually Converge?](https://arxiv.org/abs/1801.04406).
+    [Which Training Methods for GANs do actually Converge?](https://papers.labml.ai/paper/1801.04406).
 
     $$R_1(\psi) = \frac{\gamma}{2} \mathbb{E}_{p_\mathcal{D}(x)}
     \Big[\Vert \nabla_x D_\psi(x)^2 \Vert\Big]$$