SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning | ScienceToStartup | ScienceToStartup

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References (30)

[1]

SSPNet: Semi-Supervised Prototypical Networks for Few-Shot Network Intrusion Detection

2025U. Suleymanov, Murat Kantarcioglu et al.

[2]

Few-Shot Class-Incremental Learning for Network Intrusion Detection Systems

2024Davide Di Monda, Antonio Montieri et al.

[3]

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment

Founder's Pitch

"SPRINT offers state-of-the-art few-shot class-incremental learning for tabular data, addressing challenges in domains like cybersecurity and healthcare."

Few-Shot Learning•Score: 7•View PDF ↗

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SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning

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References (30)

Founder's Pitch

"SPRINT offers state-of-the-art few-shot class-incremental learning for tabular data, addressing challenges in domains like cybersecurity and healthcare."

Commercial Viability Breakdown

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Why It Matters

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SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning

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Build This Paper

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References (30)

Founder's Pitch

"SPRINT offers state-of-the-art few-shot class-incremental learning for tabular data, addressing challenges in domains like cybersecurity and healthcare."

Commercial Viability Breakdown

Related Papers

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