arXiv subjects

Yen-Cheng Liu

Publications and source records attributed to Yen-Cheng Liu.

26 records · Page 2Linked to original sources

arXiv2019

Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Continual learning has received a great deal of attention recently with several approaches being proposed. However, evaluations involve a diverse set of scenarios making meaningful comparison difficult. This work provides a systematic categorization of the scenarios and evaluates them within a consistent framework including strong baselines and state-of-the-art methods. The results provide an understanding of the relative difficulty of the scenarios and that simple baselines (Adagrad, L2 regularization, and naive rehearsal strategies) can surprisingly achieve similar performance to current mainstream methods. We conclude with several suggestions for creating harder evaluation scenarios and future research directions. The code is available at https://github.com/GT-RIPL/Continual-Learning-Benchmark

cs.LG↗

arXiv2018

A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network is able to perform continuous cross-domain image translation and manipulation, and produces desirable output images accordingly. In addition, the resulting feature representation exhibits superior performance of unsupervised domain adaptation, which also verifies the effectiveness of the proposed model in learning disentangled features for describing cross-domain data.

cs.CV↗

arXiv2018

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.

cs.CV↗

arXiv2018

Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification

Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.

cs.CV↗

arXiv2017

Detection of Spatially-Modulated Signals in Doubly Selective Fading Channels With Imperfect CSI

To detect spatially-modulated signals, a receiver needs the channel state information (CSI) of each transmit- receive antenna pair. Although the CSI is never perfect and varies in time, most studies on spatial modulation (SM) systems assume perfectly known CSI and time-invariant channel. The spatial correlations among multiple spatial subchannels, which have to be considered when CSI is imperfect, are also often neglected. In this paper, we release the above assumptions and take the CSI uncertainty along with the spatial-temporal selectivities into account. We derive the channel estimation error aware maximum likelihood (CEEA-ML) detectors as well as several low- complexity alternatives for PSK and QAM signals. As the CSI uncertainty depends on the channel estimator used, we consider both decision feedback and model based estimators in our study. The error rate performance of the ML and some suboptimal detectors is analyzed. Numerical results obtained by simulations and analysis show that the CEEA-ML detectors offer clear performance gain against conventional mismatched SM detectors and, in many cases, the proposed suboptimal detectors incur only minor performance loss.

cs.IT↗

arXiv2016

Reduced-Rank Channel Estimation for Large-Scale MIMO Systems

We present two reduced-rank channel estimators for large-scale multiple-input, multiple-output (MIMO) systems and analyze their mean square error (MSE) performance. Taking advantage of the channel's transform domain sparseness, the estimators yield outstanding performance and may offer additional mean angle-of-arrival (AoA) information. It is shown that, for the estimators to be effective, one has to select a proper predetermined unitary basis (transform) and be able to determine the dominant channel rank and the associated subspace. Further MSE analysis reveals the relations among the array size, channel rank, signal-to-noise ratio (SNR), and the estimators' performance. It provides rationales for the proposed rank determination and mean AoA estimation algorithms as well. An angle alignment operation included in one of our channel models is proved to be effective in further reducing the required rank, shifting the dominant basis vectors' range (channel spectrum) and improving the estimator's performance when a suitable basis is used. We also draw insightful analogies among MIMO channel modeling, transform coding, parallel spatial search, and receive beamforming. Computer experiment results are provided to examine the numerical effects of various estimator parameters and the advantages of the proposed channel estimators and rank determination method.

cs.IT↗

arXiv2013

Composite Channel Estimation in Massive MIMO Systems

We consider a multiuser (MU) multiple-input multiple-output (MIMO) time-division duplexing (TDD) system in which the base station (BS) is equipped with a large number of antennas for communicating with single-antenna mobile users. In such a system the BS has to estimate the channel state information (CSI) that includes large-scale fading coefficients (LSFCs) and small-scale fading coefficients (SSFCs) by uplink pilots. Although information about the former FCs are indispensable in a MU-MIMO or distributed MIMO system, they are usually ignored or assumed perfectly known when treating the MIMO CSI estimation problem. We take advantage of the large spatial samples of a massive MIMO BS to derive accurate LSFC estimates in the absence of SSFC information. With estimated LSFCs, SSFCs are then obtained using a rank-reduced (RR) channel model which in essence transforms the channel vector into a lower dimension representation. We analyze the mean squared error (MSE) performance of the proposed composite channel estimator and prove that the separable angle of arrival (AoA) information provided by the RR model is beneficial for enhancing the estimator's performance, especially when the angle spread of the uplink signal is not too large.

cs.IT↗

arXiv2013

New Constructions of Zero-Correlation Zone Sequences

In this paper, we propose three classes of systematic approaches for constructing zero correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size ($K$) and the ZCZ width ($T$) for a given sequence period ($N$). Our approaches can produce various binary and polyphase ZCZ families with desired parameters $(N,K,T)$ and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure.

cs.IT↗