Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, timeconsuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a foregroundbackground segmentation network. In particular, we learn a generative adversarial network that decomposes an image into foreground and background layers, and avoid trivial decompositions by maximizing mutual information between generated images and latent variables. The improved layered GANs can synthesize higher quality datasets from which segmentation networks of higher performance can be learned. Moreover, the segmentation networks are employed to stabilize the training of layered GANs in return, which are further alternately trained with Layered GANs. Experiments on a variety of single-object datasets show that our method achieves competitive generation quality and segmentation performance compared to related methods.

Controllable tailoring and understanding the phase-structure relationship of the 1T phase two-dimensional (2D) materials are critical for their applications in nanodevices. The in situ transmission electron microscope (TEM) could regulate and monitor the evolution process of the nanostructure of 2D material with atomic resolution. In this work, a controllably tailoring 1T-CrTe2 nanopore is carried out by the in situ TEM. A preferred formation of the 1T-CrTe2 border structure and nanopore healing process are studied at the atomic scale. The controllable tailoring of the 1T phase nanopore could be achieved by regulating the transformation of two types of low indices of crystal faces { 10 1 ¯ 0 } and { 11 2 ¯ 0 } at the nanopore border. Machine learning is applied to automatically process the TEM images with high efficiency. By adopting the deep-learning-based image segmentation method and augmenting the TEM images specifically, the nanopore of the TEM image could be automatically identified and the evaluation result of DICE metric reaches 93.17% on test set. This work presents the unique structure evolution of 1T phase 2D material and the computer aided high efficiency TEM data analysis based on deep learning. The techniques applied in this work could be generalized to other materials for controlled nanostructure regulation and automatic TEM image analyzation.

We propose an unsupervised foreground-background segmentation method via training a segmentation network on the synthetic pseudo segmentation dataset generated from GANs, which are trained from a collection of images without annotations to explicitly disentangle foreground and background. To efficiently generate foreground and background layers and overlay them to compose novel images, the construction of such GANs is fulfilled by our proposed Equivariant Layered GAN, whose improvement, compared to the precedented layered GAN, is embodied in the following two aspects. (1) The disentanglement of foreground and background is improved by extending the previous perturbation strategy and introducing private code recovery that reconstructs the private code of foreground from the composite image. (2) The latent space of the layered GANs is regularized by minimizing our proposed equivariance loss, resulting in interpretable latent codes and better disentanglement of foreground and background. Our methods are evaluated on unsupervised object segmentation datasets including Caltech-UCSD Birds and LSUN Car, achieving state-of-the-art performance.

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. Specifically, it targets minimizing the mutual information between visible and invisible regions of the foreground and background to spur interlayer independence. Through in-depth theoretical and experimental analyses, we justify that explicit layer independence modeling is critical to suppressing information leakage and contributes to impressive segmentation performance gains. Also, our ILSGAN achieves strong state-of-the-art generation quality and segmentation performance on complex real-world data.