• Weakly Supervised Computer Vision
  • Trustworthy AI
  • Data Science for Health in Africa
  • Geospatial Machine Learning: Research and Social Impact Use case
  • Robot Learning for Africa
  • Ro’ya Computer Vision for Africa
  • Mathematics for Machine Learning
  • Building a Global Network of AI Researchers on AU and UN SDGs
  • Building Bridges: People-Centred MLOps in Africa
  • Efficiency in African NLP

Please see below for information on each of these:

Weakly Supervised Computer Vision

Workshop day: 8th September

Website: https://wscv-indaba.github.io/ 

CFP: https://drive.google.com/file/d/1r3K67SFVbccJd7VfPf6Crt6DI1al2BP1/view?usp=sharing


To understand scenes from images, video or 3D data, computer vision often relies on models trained on large datasets. But as the field matured, algorithms are now expected to perform in the real-world, beyond training conditions. Hence, new tasks emerged like generalisation, open-world vision which seeks to adjust to unseen conditions (lighting, weather, etc.), robustness to adversarial attacks, image generation, etc. Weakly-supervised learning helps address these challenges because it relaxes the need of costly annotations and minimises the biases of training datasets. Thus, truly paving the way to real-world applications like autonomous driving, mobile robotics, virtual reality, image generation…

This workshop will deep dive into the latest research with talks from renowned speakers. Among others, we will address techniques like vision-language model (VLM), transfer learning, diffusion models, contrastive learning, vision transformers (ViT), continual learning, neural fields, and else; while revolving on how to relax supervision (less labels, less data), adapt to unseen data, or benefit from other modalities (text, image + text, video + text).

Trustworthy AI

Workshop day: 8th September

Website: https://trustaideepindaba.github.io/ 

CFP: https://trustaideepindaba.github.io/2023/06/07/cfp/ 


Recent years have seen an overwhelming body of work on fairness and robustness in machine learning (ML) models. This is not unexpected, as it is an increasingly important concern as ML models are used to support decision-making in high-stakes applications such as mortgage lending, hiring, and diagnosis in healthcare. Trustworthy AI aims to provide an explainable, robust, and fair decision-making process. In addition, transparency and security also play a significant role in improving the adoption and impact of ML solutions. Currently, most ML models assume ideal conditions and rely on the assumption that test/clinical data comes from the same distribution of the training samples. However, this assumption is not satisfied in most real-world applications; in a clinical setting, we can find different hardware devices, diverse patient populations, or samples from unknown medical conditions. On the other hand, we need to assess potential disparities in outcomes that can be translated and deepened in our ML solutions. Particularly, data and models are often imported from external sources in addressing solutions in developing countries, thereby risking potential security issues. The divergence of data and model from a population at hand also poses a lack of transparency and explainability in the decision-making process.

Data Science for Health in Africa

Workshop day: 8th September

Website: https://www.sisonkebiotik.africa/events/workshops/dl-indaba-2023 


The current state of healthcare in Africa is one that is currently embroiled in many challenges and this workshop aims to bring together researchers, practitioners, and healthcare professionals in Africa to discuss the potential of deep learning in improving healthcare outcomes. The workshop seeks to bring together researchers and communities working on different problems associated with healthcare using data science, deep learning, bioinformatics, among others. It will also address the challenges and opportunities for deep learning in the African healthcare context, such as data privacy, access to resources and cultural considerations. Through presentations, panel discussions, and interactive sessions, participants will gain a deeper understanding of the possibilities and limitations of deep learning in healthcare, and identify opportunities for collaboration and future research.

Geospatial Machine Learning: Research and Social Impact Use Case

Workshop day: 8th September

Website: https://dsn.ai/dl-indaba-2023/

Many African nations have made Sustainable Development a top priority due to the rise in natural disasters and the persistent issues of poverty, unemployment, and inadequate basic amenities. Unlike in the past, when access to technological infrastructure was scarce, technology is now being utilised to tackle these challenges. One of the solutions that Africa has employed to achieve sustainable development is geospatial applications. Geospatial data has proven helpful in addressing information gaps on the continent. The continent is utilising satellite data to improve conservation practices, understand the geo-demographic profile of the community and optimise social interventions with better precision. Anonymised mobile data from network operators were used to address COVID-19 challenges and drive lockdown effectiveness, while its extrapolation with large-scale points of interest dataset (funded by the Bill and Melinda Gates Foundation in Africa) is providing new insights on how to understand settlements, human migration, disease hotspots, microplanning for routine immunisation and many other health service delivery use cases. One of the key datasets for geospatial development use cases is satellite images. With numerous satellites orbiting the earth and collecting vast amounts of data every day, this can serve different purposes including agricultural pest and disease management, cropland and forest monitoring, disaster response, and damage assessment. These satellites have different sensing instruments that collect visible imagery or infrared imagery of the earth which are processed and stored in many different formats such as Hierarchical Data Format (HDF), Geographic Tagged Image File Format (GeoTIFF), Network Common Data Form (NetCDF) and Extensible Markup Language (XML). While it is possible to access a collection of major satellite data through Google Earth Engine, there are some limitations that would necessitate one to access the data from its original source. This requires knowledge of a set of tools and libraries that can help one read and process the different satellite data formats. Thereafter it can be used for various downstream machine learning tasks that benefit humanity. We are excited to propose a workshop on Geospatial Machine Learning: Research and Social Impact Use Cases at the Deep Learning Indaba 2023. The objective of this workshop is to bring together researchers and practitioners interested in the intersection of geospatial AI, remote sensing, satellite image datasets, earth observation, geo-enabled health service delivery, and geo-informed natural language processing (NLP) on the innovative works that have been done on geospatial and social


Robot Learning for Africa

Workshop day: 8th September 2023

Website: https://sites.google.com/view/robotlearning4africa

CFP: https://forms.gle/KrGBSsAyzi5zMuxr7


Robotics has immense potential to solve many of Africa’s pressing challenges across agriculture, healthcare, manufacturing, and other sectors. The implementation of robotics technologies in these sectors has the potential to increase efficiency, reduce costs, and improve productivity. However, the development of the robotics field in Africa has been slower than in other parts of the world due to the lack of funding, research infrastructure, and access to technologies.

Despite these challenges, there is a growing interest in robotics in Africa, and many researchers and practitioners are exploring opportunities to leverage the power of automation and artificial intelligence to address pressing challenges. The Robot Learning for Africa Workshop aims to provide a platform for these individuals to come together to share knowledge, exchange ideas, and collaborate on new research projects. By fostering interdisciplinary collaborations and creating networks between academia and industry, we hope to build a community of robotics researchers that will support and promote the development of practical and sustainable robotics solutions for Africa.

Ro’ya Computer Vision for Africa

Workshop day: 9th September

Website: https://ro-ya-cv4africa.github.io/homepage/event_workshop.html 


The 1st CV4Africa Workshop will be held this year co-located with Deep learning Indaba 2023. The workshop will feature invited talks from prominent researchers and practitioners, a challenge and hand-on tutorials. We invite all members of the Computer Vision community to attend the workshop. Computer vision is used in various applications that impact African communities such as, precision agriculture, satellite imagery understanding, and medical image processing. It is concerned with the mathematical techniques that include both classical and machine learning based methods towards achieving the goal of scene and video understanding, and recovering the 3D shape and appearance of objects in images. Different sub-tasks in Computer Vision include optical flow, motion detection, tracking, segmentation/grouping, and 3D reconstruction among others. Although it is widely used to serve our communities, there exists a current gap in the community based research that lacks focus on Computer Vision in Africa. We are mainly inspired by other grassroots initiatives in the African community for both natural language processing (Masakhane) and machine learning for health (Sisonke Biotik). We aim to launch a community which we call Ro’ya-CV4Africa that focuses on Computer Vision research for Africans and by Africans. We believe that this bottom-up community based approach is better able to bring researchers from varying parts of the society and is inclusive by design. Participatory research, unlike conventional research, defines the research process itself within a collaborative and accessible framework to all members of the community.

Mathematics for Machine Learning

Workshop day: 9th September

Website: https://sites.google.com/view/math4ml2023/home


Machine Learning (ML) has motivated a lot of new research in mathematics. This research uses tools from both pure and applied mathematics. The workshop will showcase some of the recent mathematical research motivated by problems coming from ML and some of the mathematical developments leveraging tools from machine learning to improve scientific discovery and engineering applications.

These include, but are not limited to:

  • PINNs (a neural networks method for numerically solving differential equations)
  • Optimisation for ML (local vs global minima, convergence of SGD, IP and MIP formulations of deep neural networks, robust optimisation)
  • Recent developments in Statistical Learning (generalisation, over-parameterisation)
  • Probabilistic approaches (Bayesian ML, transfer learning)
  • Graph theory and related topics (Knowledge graphs, Graph Neural Networks)
  • Equation Learning (symbolic regression)
  • Topological data analysis

Building a Global Network of AI Researchers on AU and UN SDGs

Workshop day: 9th September

Website: https://naixus.net/index.php/workshop-ai4sdg/ 

CFP: https://naixus.net/index.php/workshop-ai4sdg/ 


The motivation supporting this workshop is that contributing to the SDGs through AI could represent a win-win approach in which not only the goals themselves receive due attention but moreover this attention will be shared and integrated and allow a fruitful North-South cooperation. The workshop will provide a forum for the presentation and discussion of analyses of projects, initiatives and existing research networks with emphasis on identifying a portfolio of AI projects addressing SDGs. Particular interest will be placed on presentations that consider alternative ways of using AI for SDGs where it can be argued that they will aim towards creating communities around the SDG themes and thus introduce the sustainability and scalability of AI research networks. The workshop will focus on understanding how and when AI is successful in addressing the different SDG challenges and will aim at providing opportunities to integrate more research expertise – and more diverse research expertise in these tasks.

Building Bridges: People-Centred MLOps in Africa

Workshop day: 9th September

Website: https://sites.google.com/view/buildingbridgesworkshop/home

CFP: Not Applicable


For many years now the Deep Learning Indaba has been an incredible place to learn and grow in the academic space. It is time now to use this knowledge to envision and implement our future truth. We recognise the huge potential of AI in Africa and envision a future whereby African communities’ needs and aspirations are the center of AI product design. The majority of Africa’s AI innovators and entrepreneurs are Western-centric and/or Western educated without the privilege of learning how to domesticate the largely Westernised AI concepts and principles. They, therefore, might end up designing products and services that do not speak to the specificities in Africa. The People-centred MLOps workshop is one that aims at unpacking the gap between AI research and deployment paying particular attention to the uniqueness of African communities and infrastructures.

In this interactive workshop, we will figure out how to bridge this gap. On our side, we will bring together the practical implementation tools to turn ML based ideas into working products (MLOPs) as well as the best practices of making your products people and community-centred. On your side, bring your voice, expertise, experiences, and African dreams as you engage in our hands-on activities.  Together, we will co-create knowledge on AI African products that is currently missing and that we hope we can share with attendees of the Indaba and beyond.

Efficiency in African NLP

Workshop day: 8th September

Website: https://sites.google.com/view/nlpdlindaba/2023

CFP: Not applicable.


The Efficiency in African NLP Workshop brings together diverse participants to explore and advance Natural Language Processing (NLP) in Africa. With the theme “We Can: Innovate, Integrate, and Preserve AfricaNLP,” we aim to provide an overview of cutting-edge topics, efficiency in NLP and foster connections among attendees. Join us to network, receive feedback, and enhance your skills in NLP. We aim to explore challenges of low-resource languages (LRL) and efficiency in NLP. This includes discovering available resources, leveraging appropriate technologies, and accessing mentorship to embark on your NLP journey.