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Found 9 papers, 4 papers with code
NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata
We demonstrate the effectiveness of our approach in preserving the consistency of NCA dynamics across a wide range of spatio-temporal granularities.
Emergent Dynamics in Neural Cellular Automata
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA).
Mesh Neural Cellular Automata
We propose Mesh Neural Cellular Automata (MeshNCA), a method that directly synthesizes dynamic textures on 3D meshes without requiring any UV maps.
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos.
CLIPasso: Semantically-Aware Object Sketching
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings.
Optimizing Latent Space Directions For GAN-based Local Image Editing
Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes.
[Re] Warm-Starting Neural Network Training
We reproduce the results of the paper ”On Warm-Starting Neural Network Training.” In many real-world applications, the training data is not readily available and is accumulated over time.
ChOracle: A Unified Statistical Framework for Churn Prediction
In this approach which is more realistic in real world online services, at each time-step the model predicts the user return time instead of predicting a churn label.
Back to square one: probabilistic trajectory forecasting without bells and whistles
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources.
