Ivan Mashkin

Papers

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About

Ivan Mashkin is a researcher advancing the frontier of lifelong machine learning, with a primary focus on object recognition and continual learning systems. His most cited work, "Towards lifelong object recognition: A dataset and benchmark" (2022), has garnered 8 citations and addresses a critical challenge in AI: enabling models to learn incrementally without catastrophic forgetting. By introducing a specialized dataset and evaluation framework, Mashkin provides the community with essential tools for benchmarking algorithms that must adapt to new visual categories over time. This contribution is foundational for developing autonomous systems—such as robots and augmented reality devices—that operate in dynamic, open-world environments. Beyond this benchmark, Mashkin’s research explores how neural networks can retain knowledge while acquiring new skills, bridging the gap between static training and real-world deployment. His work is particularly relevant for students and researchers interested in continual learning, computer vision, and the practical challenges of deploying AI in long-lived applications. With a growing citation footprint, Mashkin is establishing himself as a key voice in making machine intelligence more adaptive and robust.