Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same distribution.
Jan 12, 2024
Jan 12, 2024 · In this paper, we propose a novel method called “Direct Distillation between Different Domains” (4Ds), which straightforwardly trains a student ...
Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are ...
Direct Distillation between Different Domains ... Then, we build a fusion-activation mechanism to transfer the valuable domain-invariant knowledge to the student ...
In this paper, we proposed a three-step Progressive Cross-domain Knowledge Distillation (PCdKD) paradigm for efficient unsupervised adaptive object detection, ...
We propose a simple yet effective method for domain generalization, named cross-domain ensemble distil- lation (XDED), that learns domain-invariant features ...
Jul 25, 2024 · Domain generalization (DG) aims to generalize the knowledge learned from multiple source domains to unseen target domains.
The assigment is worked on a binary classification data of 3 different domains. There are 4 different variations of the data based on how close the centroid of ...
Missing: Direct | Show results with:Direct
... Direct Distillation between Different Domains" (4Ds). We first design a learnable adapter based on the Fourier transform to separate the domain-invariant ...
1) we propose a new approach for joint KD and UDA that allows training CNNs such that they generalize well on one or multiple target domains; 2) we introduce a ...