Mar 28, 2024 · This paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts.
Data-Free Knowledge Distillation (DFKD) is a promis- ing task to train high-performance small models to enhance actual deployment without relying on the ...
Mar 28, 2024 · This toy experiment aims to show the distribution shifts between the substitution and original data. These methods include generation with ...
Data-Free Knowledge Distillation (DFKD) is a promis- ing task to train high-performance small models to enhance actual deployment without relying on the ...
Algorithm 1 Training process of generation-based methods combined with our KDCI. Input: A pre-trained teacher model T , a generator g, a student.
This paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts.
Sep 22, 2024 · Compact data is a method that optimizes the big dataset that gives best assets without handling complex bigdata. The compact dataset contains ...
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original ...
The core message of this paper is to introduce a novel causal inference perspective to handle the distribution shifts between the substitution and original data ...
Oct 31, 2022 · We propose a simple yet effective method called Momentum Adversarial Distillation (MAD) which maintains an exponential moving average (EMA) copy of the ...