TensorFlow Research Models
This folder contains machine learning models implemented by researchers in TensorFlow. The models are maintained by their respective authors. To propose a model for inclusion, please submit a pull request.
Currently, the models are compatible with TensorFlow 1.0 or later. If you are running TensorFlow 0.12 or earlier, please upgrade your installation.
Models
adversarial_crypto: protecting communications with
adversarial neural cryptography.
adversarial_text: semi-supervised sequence learning with
adversarial training.
attention_ocr: a model for real-world image text
extraction.
autoencoder: various autoencoders.
brain_coder: Program synthesis with reinforcement learning.
cognitive_mapping_and_planning:
implementation of a spatial memory based mapping and planning architecture
for visual navigation.
compression: compressing and decompressing images using a
pre-trained Residual GRU network.
deeplab: deep labelling for semantic image segmentation.
delf: deep local features for image matching and retrieval.
differential_privacy: differential privacy for training
data.
domain_adaptation: domain separation networks.
gan: generative adversarial networks.
im2txt: image-to-text neural network for image captioning.
inception: deep convolutional networks for computer vision.
learning_to_remember_rare_events: a
large-scale life-long memory module for use in deep learning.
learning_unsupervised_learning: a
meta-learned unsupervised learning update rule.
lexnet_nc: a distributed model for noun compound relationship
classification.
lfads: sequential variational autoencoder for analyzing
neuroscience data.
lm_1b: language modeling on the one billion word benchmark.
maskgan: text generation with GANs.
namignizer: recognize and generate names.
neural_gpu: highly parallel neural computer.
neural_programmer: neural network augmented with logic
and mathematic operations.
next_frame_prediction: probabilistic future frame
synthesis via cross convolutional networks.
object_detection: localizing and identifying multiple
objects in a single image.
pcl_rl: code for several reinforcement learning algorithms,
including Path Consistency Learning.
ptn: perspective transformer nets for 3D object reconstruction.
qa_kg: module networks for question answering on knowledge graphs.
real_nvp: density estimation using real-valued non-volume
preserving (real NVP) transformations.
rebar: low-variance, unbiased gradient estimates for discrete
latent variable models.
resnet: deep and wide residual networks.
skip_thoughts: recurrent neural network sentence-to-vector
encoder.
slim: image classification models in TF-Slim.
street: identify the name of a street (in France) from an image
using a Deep RNN.
swivel: the Swivel algorithm for generating word embeddings.
syntaxnet: neural models of natural language syntax.
tcn: Self-supervised representation learning from multi-view video.
textsum: sequence-to-sequence with attention model for text
summarization.
transformer: spatial transformer network, which allows the
spatial manipulation of data within the network.
video_prediction: predicting future video frames with
neural advection.
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