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.

• audioset: Models and supporting code for use with

  AudioSet.

• 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.