AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) / Kuan-Chuan Peng, Ziyan Wu.

Format:

Book

Author/Creator:

Peng, Kuan-Chuan, author.

Wu, Ziyan, author.

Language:

English

Subjects (All):

Artificial intelligence--Congresses.

Artificial intelligence.

Physical Description:

1 electronic resource (186 p.)

Other Title:

AAAI Workshop on Artificial Intelligence with Biased or Scarce Data

Place of Publication:

Basel MDPI - Multidisciplinary Digital Publishing Institute 2022

Basel : MDPI - Multidisciplinary Digital Publishing Institute, 2022.

Language Note:

English

Summary:

This book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this book.

Contents:

About the Editors

Statement of Peer Review

Electricity Consumption Forecasting for Out-of-Distribution Time-of-Use Tariffs

Measuring Embedded Human-Like Biases in Face Recognition Models

Measuring Gender Bias in Contextualized Embeddings

The Details Matter: Preventing Class Collapsein Supervised Contrastive Learning

DAP-SDD: Distribution-Aware Pseudo Labeling for Small Defect Detection

Quantifying Bias in a Face

Verification System

Super-Resolution for Brain MR Images from a Significantly Small Amount of Training Data

Dual Complementary Prototype Learning for Few-Shot Segmentation

Extracting Salient Facts from Company Reviews with Scarce Labels

Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data

Age Should Not Matter:

Towards More Accurate Pedestrian Detection via Self-Training.

Notes:

Description based on publisher supplied metadata and other sources.