Tiny RNN Autonomously Generates Full Bad Apple Video
💡See how 417k parameters replay 6,500 video frames autonomously from one learned state.
⚡ 30-Second TL;DR
What Changed
The closed-loop model evolves 64-dimensional hidden and memory states, then decodes each state into a full-resolution frame.
Why It Matters
The project demonstrates that a small recurrent latent system can memorize and replay a long visual sequence without explicit time conditioning. Its training techniques may be useful for compact video generation, dynamical-system modeling, and autonomous sequence rollouts, although the result is specialized to a single memorized video.
What To Do Next
Clone the SEBADA321/BadAppleRNN repository and reproduce its rollout-horizon curriculum on a short custom video before adapting the method to your own sequence-generation task.
Key Points
- •The closed-loop model evolves 64-dimensional hidden and memory states, then decodes each state into a full-resolution frame.
- •The recurrent transition has 16,640 parameters, while the depthwise-separable frame decoder has 400,361 parameters.
- •Training uses learned latent teacher tables, rollout-horizon curriculum from 2 to 512 steps, state perturbation noise, and acceleration regularization.
- •The initial state contains only 128 floating-point values, while the teacher tables are discarded before inference.
- •Code, weights, rollout scripts, plots, and standalone models are available in the SEBADA321/BadAppleRNN GitHub repository.
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