You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
A practical face recognition system demands not only high recognition performance but also the capability of detecting spoofing attacks. While emerging approaches to face antispoofing have been proposed in recent years, most of them perform poorly on unseen samples. The generalizability of face antispoofing needs to be significantly improved before it can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.g., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials makes the spoofing attacks quite different. Another reason for the poor generalizability is that limited labeled data are available for training for face antispoofing. We focus on improving the generalizability of convolutional neural network (CNN)-based face antispoofing methods across different kinds of datasets. We propose a deep domain transfer CNN using sparsely unlabeled data from the target domain to learn features that are invariant across domains for face antispoofing. Experiments on five face spoofing datasets show that the proposed method significantly improves the cross-test performance only with a small number of unlabeled samples from the target domain.

Cited Abstract(s):
[1]: In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.

[2]: 

[3]: We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set.

[4]: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57 error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28 relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

[5]: Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.

[6]:
To bridge the gap between two different distributions, many approaches have been proposed based on the ideas of domain adaptation [5] [1] [2] [6] . Multimodal deep learning architectures have been proposed to learn domain invariant representations in [4] . However, this method performed primarily in a generative context and did not leverage the full representation power of supervised CNN representations. In [3] , proposed pre-training with a denoising auto encoder, then train a two-layer network simultaneously with the MMD domain confusion loss. Nevertheless, the learned network is relatively shallow and therefore lacks the strong semantic representation which is learned by directly optimizing with a supervised deep CNN. In [2] , proposed a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss for classification. They use MMD both to select the depth and width of the architecture while using it as a regularizer during fine-tuning, and achieved state-of-the-art performance on the standard visual domain adaptation benchmark.