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Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning
  in Encrypted Traffic Classification

Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification

21 May 2023
Idio Guarino
Chao Wang
A. Finamore
A. Pescapé
Dario Rossi
ArXivPDFHTML

Papers citing "Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification"

5 / 5 papers shown
Title
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Jan Luxemburk
Karel Hynek
Richard Plný
T. Čejka
45
0
0
18 Feb 2025
Data Augmentation for Traffic Classification
Data Augmentation for Traffic Classification
Chao Wang
A. Finamore
Pietro Michiardi
Massimo Gallo
Dario Rossi
35
6
0
19 Jan 2024
Replication: Contrastive Learning and Data Augmentation in Traffic
  Classification Using a Flowpic Input Representation
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
A. Finamore
Chao Wang
J. Krolikowski
J. M. Navarro
Fuxing Chen
Dario Rossi
16
9
0
18 Sep 2023
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
320
11,681
0
09 Mar 2017
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
163
25,247
0
09 Jun 2011
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