Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Zero and Few-Shot Transfer for Facial Forgery Detection   We propose Deep Distribution Transfer(DDT), a new transfer learning approach
to address the problem of zero and few-shot transfer in the context of facial
forgery detection. We examine how well a model (pre-)trained with one forgery
creation method generalizes towards a previously unseen manipulation technique
or different dataset. To facilitate this transfer, we introduce a new mixture
model-based loss formulation that learns a multi-modal distribution, with modes
corresponding to class categories of the underlying data of the source forgery
method. Our core idea is to first pre-train an encoder neural network, which
maps each mode of this distribution to the respective class labels, i.e., real
or fake images in the source domain by minimizing wasserstein distance between
them. In order to transfer this model to a new domain, we associate a few
target samples with one of the previously trained modes. In addition, we
propose a spatial mixup augmentation strategy that further helps generalization
across domains. We find this learning strategy to be surprisingly effective at
domain transfer compared to a traditional classification or even
state-of-the-art domain adaptation/few-shot learning methods. For instance,
compared to the best baseline, our method improves the classification accuracy
by 4.88% for zero-shot and by 8.38% for the few-shot case transferred from the
FaceForensics++ to Dessa dataset.

3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg