Tensorflow Research Models
MM 05/24/2018
The files are:
-adv_imagenet_models/
-adversarial_cryptto/
-adversarial_text/
-astronet/
-attention_ocr/
-audioset/
-brain_coder/
-cognitive_mapping_and_planning/
-compression/
-deeplab/
-delf/
-differential_privacy/
-domain_adaptation/
-fivo/
-gan/
-im2txt/
-inception/
-learned_optimizaer/
-learning_to_remember_rare_events/
-learning_unsupervised_learning/
-lexnet_nc/
-lfads/
-lm_ab/
-marco/
-maskgan/
-minigo/
-morph_net/
-namignizer/
-neural_gpu/
-neural_programmer/
-next_frame_prediction/
-object_detection/
-pcl_rl/
-ptn/
-qa_kg/
-real_nvp/
-rebar/
-resnet/
-skip_throughts/
-slim/
-street/
-swivel/
-syntaxnet/
-tcn/
-tensorrt/
-textsum/
-transformer/
-video_prediction/
-README.md, -setup.py
README.md
This folder contains machine learning models implemented by researchers inTensorflow.
The models are maintained by their respective authors.
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 labeling 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.
- marco : automating the evaluation of crystallization experiments.
- 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_throughts : 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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