update examples readme

examples/README.md

@@ -5,37 +5,47 @@ Training examples with __reproducible__ and meaningful performance.
 
 ## Getting Started:
 + [An illustrative mnist example with explanation of the framework](mnist-convnet.py)
-+ The same mnist example using [tf-slim](mnist-tfslim.py), [Keras](mnist-keras.py), and [with weights visualizations](mnist-visualizations.py)
++ The same mnist example using [tf-slim](mnist-tfslim.py), [Keras layers](mnist-keras.py), [Higher-level Keras](mnist-keras-v2.py) and [with weights visualizations](mnist-visualizations.py)
++ [A tiny SVHN ConvNet with 97.8% accuracy](svhn-digit-convnet.py)
 + [A boilerplate file to start with, for your own tasks](boilerplate.py)
 
 ## Vision:
-+ [A tiny SVHN ConvNet with 97.8% accuracy](svhn-digit-convnet.py)
-+ Train [ResNet](ResNet) and [ShuffleNet](ShuffleNet) on ImageNet
-+ [Train ResNet50-Faster-RCNN on COCO](FasterRCNN)
-+ [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net)
-+ [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN
-+ [Inception-BN and InceptionV3](Inception)
-+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED)
-+ [Spatial Transformer Networks on MNIST addition](SpatialTransformer)
-+ [Visualize CNN saliency maps](Saliency)
-+ [Similarity learning on MNIST](SimilarityLearning)
-+ Learn steering filters with [Dynamic Filter Networks](DynamicFilterNetwork)
-+ Load a pre-trained [AlexNet](load-alexnet.py) or [VGG16](load-vgg16.py) model
-+ Load a pre-trained [Convolutional Pose Machines](ConvolutionalPoseMachines/)
+| Name | Performance |
+| ---  | --- |
+|	Train [ResNet](ResNet) and [ShuffleNet](ShuffleNet) on ImageNet		| reproduce paper	|
+|	[Train ResNet50-Faster-RCNN on COCO](FasterRCNN)				|	reproduce paper		|
+| [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net) | reproduce paper |
+| [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, <br/> Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN | visually reproduce |
+| [Inception-BN and InceptionV3](Inception) | reproduce reference code |
+| [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED) | visually reproduce |
+| [Spatial Transformer Networks on MNIST addition](SpatialTransformer) | reproduce paper |
+| [Visualize CNN saliency maps](Saliency) | visually reproduce |
+| [Similarity learning on MNIST](SimilarityLearning) | |
+| Learn steering filters with [Dynamic Filter Networks](DynamicFilterNetwork) | visually reproduce |
+| Load a pre-trained [AlexNet](load-alexnet.py) or [VGG16](load-vgg16.py) model | |
+| Load a pre-trained [Convolutional Pose Machines](ConvolutionalPoseMachines/) | |
 
 ## Reinforcement Learning:
-+ [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
-+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](A3C-Gym)
+| Name | Performance |
+| ---  | --- |
+| [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.  | reproduce paper |
+| [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](A3C-Gym) | reproduce paper |
 
 ## Speech / NLP:
-+ [LSTM-CTC for speech recognition](CTC-TIMIT)
-+ [char-rnn for fun](Char-RNN)
-+ [LSTM language model on PennTreebank](PennTreebank)
+| Name | Performance |
+| ---  | --- |
+| [LSTM-CTC for speech recognition](CTC-TIMIT) | reproduce paper |
+| [char-rnn for fun](Char-RNN) | fun |
+| [LSTM language model on PennTreebank](PennTreebank) | reproduce reference code |
 
 
-Note to contributors:
+#### Note to contributors:
 
 Example needs to satisfy one of the following:
 + Reproduce performance of a published or well-known paper.
 + Get state-of-the-art performance on some task.
 + Illustrate a new way of using the library that is currently not covered.
+
+__Performance is important__. Usually deep learning code is easy to write,
+but hard to know the correctness -- thanks to SGD things will usually still converge when you've made mistakes.
+Without a setting and performance comparable to someone else, you don't know if an implementation is correct or not.