Nips14-Ssl

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nips14-ssl is an Open Source & Models tool that provides code for reproducing results from a NIPS 2014 paper on semi-supervised learning with deep generative models. It enables researchers to implement and experiment with SSL techniques.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is nips14-ssl?

nips14-ssl offers the foundational code necessary to replicate key findings from the NIPS 2014 paper, "Semi-Supervised Learning with Deep Generative Models." This open-source repository is designed for researchers and developers interested in semi-supervised learning (SSL) using deep generative models. It includes scripts for training various models like M1 (Variational Auto-Encoder) and M1+M2 for semi-supervised experiments, as well as M2 with fully observed labels. The code also supports qualitative results such as flying through latent space and analogies, and quantitative evaluation of test-set errors. Users can configure the environment with Python, Numpy, and Theano to run experiments on datasets like MNIST and SVHN.

Best used for

Ideal for data scientists and developers who need to reproduce and validate research findings in semi-supervised learning, train deep generative models, and experiment with advanced SSL techniques. Especially valuable for academic researchers and students working on machine learning and AI.

Common actions

reproduce research results

train deep generative models

experiment with semi-supervised learning

evaluate model performance

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Capabilities

Key features

Reproduce NIPS 2014 results
M1 model training
M1+M2 semi-supervised training
M2 fully supervised training
Latent space visualization
Analogies generation
Test-set error evaluation

Target Audience

data scientistdeveloperstudentprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the prerequisites for running the nips14-ssl code?

To run the nips14-ssl code, you need Python (version 2.7 or higher), Numpy, and Theano installed. It's also recommended to set floatX=float32 in your Theano configuration for optimal performance, especially since the code is not GPU-optimized.

Can I use this code to generate qualitative results like latent space movies?

Yes, the repository includes scripts like `run_flying.py` to generate movies of flying through the latent space of the M2 model for datasets like MNIST or SVHN. You will need ffmpeg installed to save the movie files.

How can I reproduce the semi-supervised learning experiments from the paper?

You can reproduce the semi-supervised learning experiments with the M1+M2 model by running `python run_2layer_ssl.py [n_labels] [seed]`. You can specify the number of labels (e.g., 100, 600, 1000, 3000) and a random seed to match the paper's experimental setup.

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