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September, 2024

September, 2024

Comparison with SKD and ARD and Implementations of Stronger Attacker Algorithms
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
Evaluating NEO-KD Against Single-Exit Defense Methods in Multi-Exit Networks
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
Examining the Adversarial Test Accuracy of Later Exits in NEO-KD Networks
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
The Impact of Hyperparameters on Adversarial Training Performance
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
Clean Test Accuracy and Adversarial Training via Average Attack
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
Fine-Tuning NEO-KD for Robust Multi-Exit Networks
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, ...
How NEO-KD Reduces Adversarial Transferability and Improves Accuracy
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. ::: Table of Links Abstract and 1. Introduction 2. Related Works 3. Prop...
How Ensemble Strategies Impact Adversarial Robustness in Multi-Exit Networks
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, ...
How NEO-KD Saves Up to 81% of Computing Power While Maximizing Adversarial Accuracy
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, ...
Benchmarking NEO-KD on Adversarial Robustness
:::info Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, ...
Newswire > September, 2024

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