Beginner Explanation
Imagine you have a coloring book filled with pictures. Each picture is a data sample. An edit flow is like having a magic pencil that lets you change parts of the pictures without starting from scratch. You can add colors, erase some parts, or even transform shapes, all while keeping the essence of the original picture. Just like how an artist can modify their artwork, edit flows let computers modify existing data to create new samples that look similar but are different in some ways.Technical Explanation
Edit Flows are a class of generative models that leverage normalizing flows to modify existing data samples. They work by learning a mapping from a data distribution to a latent space and then back to the data space, allowing for controlled transformations. The process involves defining a series of invertible transformations that can adjust the data. For instance, using a library like TensorFlow Probability, you can define a flow model and apply transformations as follows: “`python import tensorflow as tf import tensorflow_probability as tfp def create_edit_flow(): base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0]) flow_layers = [tfp.layers.AutoregressiveNetwork(event_shape=[2])] flow_model = tfp.layers.ProbabilisticLayer(distribution=base_distribution, bijector=flow_layers) return flow_model edit_flow = create_edit_flow() modified_sample = edit_flow(sample) “` This code creates an edit flow model that can modify a given data sample by applying learned transformations.Academic Context
Edit Flows are grounded in the theory of normalizing flows, which are a class of generative models that allow for the exact computation of likelihoods and the generation of new samples through invertible transformations. Key papers include ‘Normalizing Flows for Probabilistic Modeling and Inference’ by Rezende & Mohamed (2015) and ‘Variational Inference with Normalizing Flows’ by Kingma & Dhariwal (2018). The mathematical foundation relies on the change of variables theorem, which states that the probability density function can be transformed through a series of bijections, allowing for flexible modeling of complex distributions. This approach has been instrumental in advancing generative modeling techniques in machine learning.Code Examples
Example 1:
import tensorflow as tf
import tensorflow_probability as tfp
def create_edit_flow():
base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0])
flow_layers = [tfp.layers.AutoregressiveNetwork(event_shape=[2])]
flow_model = tfp.layers.ProbabilisticLayer(distribution=base_distribution, bijector=flow_layers)
return flow_model
edit_flow = create_edit_flow()
modified_sample = edit_flow(sample)Example 2:
base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0])
flow_layers = [tfp.layers.AutoregressiveNetwork(event_shape=[2])]
flow_model = tfp.layers.ProbabilisticLayer(distribution=base_distribution, bijector=flow_layers)
return flow_modelExample 3:
Edit Flows are a class of generative models that leverage normalizing flows to modify existing data samples. They work by learning a mapping from a data distribution to a latent space and then back to the data space, allowing for controlled transformations. The process involves defining a series of invertible transformations that can adjust the data. For instance, using a library like TensorFlow Probability, you can define a flow model and apply transformations as follows:
```python
import tensorflow as tf
import tensorflow_probability as tfpExample 4:
import tensorflow as tf
import tensorflow_probability as tfp
def create_edit_flow():
base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0])Example 5:
import tensorflow_probability as tfp
def create_edit_flow():
base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0])
flow_layers = [tfp.layers.AutoregressiveNetwork(event_shape=[2])]Example 6:
def create_edit_flow():
base_distribution = tfp.distributions.MultivariateNormalDiag(loc=[0.0, 0.0])
flow_layers = [tfp.layers.AutoregressiveNetwork(event_shape=[2])]
flow_model = tfp.layers.ProbabilisticLayer(distribution=base_distribution, bijector=flow_layers)
return flow_modelView Source: https://arxiv.org/abs/2511.16599v1