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.


  • 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


  • audioset: Models and supporting code for use with


  • 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


  • 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


  • 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


  • 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


  • 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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