Unsupervised Cross Domain Distance Metric Adaptation with Feature Transfer Network

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Unsupervised domain adaptation is an attractive avenue to enhance the performance of deep neural networks in a target domain, using labels only from a source domain. However, two predominant methods along this line, namely, domain divergence reduction learning and semi-supervised learning, are not readily applicable when the source and target domains do not share a common label space. This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping the representations for the two domains well-separated. Inspired by a theoretical error bound on the target domain, we first reformulate the disjoint classification, where the source and target domains correspond to non-overlapping class labels, to a verification task. To handle both within-domain and cross-domain verification tasks, we propose a Feature Transfer Network (FTN) that separates the target features from the source features while simultaneously aligning the target features with a transformed source feature space. Moreover, we present a non-parametric variation of multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain. In experiments, we demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.

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Learning Gibbs-Regularized Pushforward Density Estimators with a Symmetric KL Objective

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We claim that there is currently no satisfactory way to regularize a generative adversarial network (GAN): neither the generator nor discriminator is particularly amenable to the imposition of inductive biases derived from domain knowledge. A generator is effectively a causal model of generation—one that usually bears no resemblance to the true generation process, which is most often unobserved or exceedingly difficult to model. Consider image generation: although it is plausible—e.g., from biological arguments—that convolutional neural networks constitute a good class of image classifiers, claiming CNNs are inherently well-suited to image generation is harder to justify. Likewise, it is clear that regularizing the discriminator is necessary to prevent trivial solutions; although recent methods have seen some success in applying generic smoothness regularizers to the discriminator [1, 5, 12], it is not obvious how to impose domain-specific structure on the discriminator in an optimal way

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Distributed Temperature and Strain Sensing Using Brillouin Optical Time Domain Reflectometry Over a Few Mode Elliptical Core Optical Fiber

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We propose a single-ended Brillouin-based sensor in elliptical-core few-mode optical fiber for multi-parameter measurement using spontaneous Brillouin scattering. Distributed sensing of temperature and strain is demonstrated over 0.5 km elliptical-core few-mode fiber.

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ELI: Empowering LTE with Interference Awareness in Unlicensed Spectrum

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The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE’s channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI’s three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.

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Optimization of Probabilistic Shaping Enabled Transceivers with Large Constellation Sizes for High Capacity Transmission

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We study digital signal processing techniques to optimize the back-to-back performance of large probabilistic shaped constellations. We cover joint optimization of LDPC and constellation shaping, CD pre-compensation, clipping and I/Q imbalance compensation.

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Neuron-Network-based Nonlinearity Compensation Algorithm

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A simplified, system-agnostic NLC algorithm based on a neuron network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6dB Q improvement after 2800km SMF transmission using 32Gbaud DP-16QAM.

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Intelligent Filtering-Penalty Monitoring and Mitigation for Cascaded WSSs using Ensemble Learning Algorithm

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An ensemble learning algorithm is applied to enhance filtering tolerance of cascaded WSSs in open ROADM environment to demonstrate ~0.8dB Q-factor improvement over MLSE after transmitting over 3200km with 16 ROADMs.

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Parametric t-Distributed Stochastic Exemplar-centered Embedding

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Parametric embedding methods such as parametric t-distributed Stochastic Neighbor Embedding (pt-SNE) enables out-of-sample data visualization without further computationally expensive optimization or approximation. However, pt-SNE favors small mini-batches to train a deep neural network but large mini-batches to approximate its cost function involving all pairwise data point comparisons, and thus has difficulty in finding a balance. To resolve the conflicts, we present parametric t-distributed stochastic exemplar-centered embedding. Our strategy learns embedding parameters by comparing training data only with precomputed exemplars to indirectly preserve local neighborhoods, resulting in a cost function with significantly reduced computational and memory complexity. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep feedforward neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed method significantly outperforms pt-SNE in terms of robustness, visual effects, and quantitative evaluations.

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Zero-Shot Object Detection

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We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories as in prior works on zero-shot classification. We present a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust detectors. One of these models uses a fixed background class and the other is based on iterative latent assignments. We also outline the challenge associated with using a limited number of training classes and propose a solution based on dense sampling of the semantic label space using auxiliary data with a large number of categories. We propose novel splits of two standard detection datasets – MSCOCO and VisualGenome, and present extensive empirical results in both the traditional and generalized zero-shot settings to highlight the benefits of the proposed methods. We provide useful insights into the algorithm and conclude by posing some open questions to encourage further research.

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Learning to Look around Objects for Top-View Representations of Outdoor Scenes

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Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view. This challenging problem not only requires an accurate understanding of both the 3D geometry and the semantics of the visible scene, but also of occluded areas. We propose a convolutional neural network that learns to predict occluded portions of the scene layout by looking around foreground objects like cars or pedestrians. But instead of hallucinating RGB values, we show that directly predicting the semantics and depths in the occluded areas enables a better transformation into the top-view. We further show that this initial top-view representation can be significantly enhanced by learning priors and rules about typical road layouts from simulated or, if available, map data. Crucially, training our model does not require costly or subjective human annotations for occluded areas or the top-view, but rather uses readily available annotations for standard semantic segmentation in the perspective view. We extensively evaluate and analyze our approach on the KITTI and Cityscapes data sets.

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