AREL
Story generator
This codebase provides an implementation of a novel adversarial reward learning algorithm for generating human-like visual stories from image sequences.
Code for the ACL paper "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling"
136 stars
12 watching
35 forks
Language: Python
last commit: over 5 years agoadversarial-learningadversarial-reward-learninginverse-reinforcement-learningrlvision-and-languagevisual-storytelling
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A toolkit for generating and analyzing adversarial triggers in natural language processing models. | 295 | |
| A high-throughput reinforcement learning library with optimized synchronous and asynchronous implementations of policy gradients. | 839 | |
| An open-source reinforcement learning library for PyTorch, providing a simple and clear implementation of various algorithms. | 402 | |
| An online tool allowing users to visualize and generate adversarial examples to deceive neural networks | 130 | |
| Scripts to generate datasets for an image generation task using Generative Adversarial Networks and deep learning techniques | 37 | |
| Replication of Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in PyTorch for reinforcement learning tasks | 830 | |
| Provides clean implementations of imitation and reward learning algorithms | 1,350 | |
| Provides Julia code to generate figures and examples for an introductory reinforcement learning book | 309 | |
| A PyTorch implementation of a deep learning-based method for generating interactive scenes with specified object attributes and relations | 188 | |
| An implementation of Adversarially Regularized Autoencoders for language generation and discrete structure modeling. | 400 | |
| A tool for generating adversarial examples to attack text classification and inference models | 496 | |
| Provides tools and algorithms for developing reinforcement learning policies in game environments. | 3 | |
| Efficient Contextual Representation Learning Model with Continuous Outputs | 4 | |
| A collection of implementations of Reinforcement Learning and planning algorithms in Python. | 596 | |
| Automates generation of discrete sequence text using adversarially regularized autoencoders | 20 |