Released Neural Network Libraries v1.36.0!

Friday, July 14, 2023


Posted by Tomonobu Tsujikawa

We have released Neural Network Libraries v1.36.0 and newly released Pixel-Guided-Diffusion!
Please see “Spotlight” for important changes.


Pixel Guided Diffusion

We have released a fine-grained image editing method using diffusion model such as pixel-guided-diffusion here!

This method enables pixel-level precise real image editing using the user-provided manipulation of semantic segmentation map.
Both pytorch and nnabla version implementations are available.

Pixel Guided Diffusion

Dataset DDPM

We also released the nnabla implementation of DatasetDDPM on nnabla-examples.

This is a label-efficient semantic segmentation model using diffusion models.
You can train segmentation models with only a few-dozen pairs of annotated data with DatasetDDPM.

[Fairness] Preferential Sampling Algorithm (part1 / part2)

We have implemented colab interactive demo for the Preferential Sampling algorithm for Discrimination-free training sets, which effectively identifies and adjusts borderline objects based on their positive class probabilities, ensuring a fair and unbiased dataset.

Name Notebook Task Example
Preferential Sampling Open In Colab Dataset/Model Bias Check and Mitigation by Preferential Sampling workflow

[Fairness] Support for the “Massage Data” algorithm on various datasets using data processor class

We have implemented colab interactive demo for the Massage Data algorithm it is a Preprocessing technique designed to mitigate bias in datasets.
This tutorial covers the step-by-step process of identifying promotion and demotion candidates, strategically relabeling objects to achieve fairness, and calculating discrimination metrics for discrimination-free datasets.

Name Notebook Task Example
Massage Data Preprocessing for UCI Adult dataset Open In Colab Massage Data Preprocessing Algorithm on the UCI Adult dataset to check and mitigate dataset/model bias workflow







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