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User seeks transformer model approach for app detection from unlabeled network data

By PulseAugur Editorial · [1 sources] ·

A user on Reddit's r/MachineLearning subreddit is seeking advice on developing a transformer model to detect applications based on unlabeled network traffic data. The user has a large volume of unlabeled data and a smaller set of labeled data for a limited number of apps. They are exploring self-supervised learning techniques like contrastive learning and masked modeling, as well as pre-training and fine-tuning approaches. A key concern is ensuring the model learns to identify apps rather than user or device-specific patterns. AI

RANK_REASON This is a user query on a forum seeking technical advice, not a news event.

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

User seeks transformer model approach for app detection from unlabeled network data

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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/AdventurousWear618 ·

    stuck on finding a approach for app detection ( making a transformer modal out of unlabeled network data) [R] [P]

    <!-- SC_OFF --><div class="md"><p>As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work ( I need to detect like 3-5K apps give or tak…

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