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Chainer Models

This repository contains a number of models implemented in Chainer.

Contributing guidelines

If you have created a model, please send us a pull request. For those just getting started with pull requests, GitHub has a howto.

We have a list of candidate papers to implement: https://github.com/chainer/models/projects/1

Models

  • Averaging Weights Leads to Wider Optima and Better Generalization [code] [paper]
  • Snapshot Ensembles: Train 1, get M for free [paper] [code]
  • Compressing Word Embeddings via Deep Compositional Code Learning [paper] [code]
  • Simple Does It: Weakly Supervised Instance and Semantic Segmentation [paper] [code]
  • Mixture Density Networks [article] [code]
  • GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks [paper] [code]
  • Improving Language Understanding by Generative Pre-Training [article] [code]
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [paper] [code]
  • Deep contextualized word representations [paper] [code]
  • Adversarial Training Methods for Semi-Supervised Text Classification [paper] [code]
  • Multi-label image classification [code]
  • Real-Time Seamless Single Shot 6D Object Pose Prediction [paper] [code]
  • Neural Relational Inference for Interacting Systems [paper] [code]

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  • Python 98.2%
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