Adversarial Discriminative Domain Adaptation

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversarial approaches to unsupervised domain adaptation have recently been introduced, which reduce the difference between the training and test domain distributions and thus improve generalization performance. Prior generative approaches show compelling visualizations, but are not optimal on discriminative tasks and can be limited to smaller shifts. Prior discriminative approaches could handle larger domain shifts, but imposed tied weights on the model and did not exploit a GAN-based loss. We first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and we use this generalized view to better relate the prior approaches. We propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard cross-domain digit classification tasks and a new more difficult cross-modality object classification task

LSDA: Large Scale Detection Through Adaptation

A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available a

Control of Myoblast Fusion by a Guanine Nucleotide Exchange Factor, Loner, and Its Effector ARF6

AbstractMyoblast fusion is essential for the formation and regeneration of skeletal muscle. In a genetic screen for regulators of muscle development in Drosophila, we discovered a gene encoding a guanine nucleotide exchange factor, called loner, which is required for myoblast fusion. Loner localizes to subcellular sites of fusion and acts downstream of cell surface fusion receptors by recruiting the small GTPase ARF6 and stimulating guanine nucleotide exchange. Accordingly, a dominant-negative ARF6 disrupts myoblast fusion in Drosophila embryos and in mammalian myoblasts in culture, mimicking the fusion defects caused by loss of Loner. Loner and ARF6, which also control the proper membrane localization of another small GTPase, Rac, are key components of a cellular apparatus required for myoblast fusion and muscle development. In muscle cells, this fusigenic mechanism is coupled to fusion receptors; in other fusion-competent cell types it may be triggered by different upstream signals