Cross Contrastive Feature Perturbation for Domain Generalization

Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. Yet, these approaches can hardly deal with the restriction that the samples synthesized from various domains can cause semantic distortion. In this paper, we propose an online one-stage Cross Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by generating perturbed features in the latent space while regularizing the model prediction against domain shift. Different from the previous fixed synthesizing strategy, we design modules with learnable feature perturbations and semantic consistency constraints. In contrast to prior work, our method does not use any generative-based models or domain labels. We conduct extensive experiments on a standard DomainBed benchmark with a strict evaluation protocol for a fair comparison. Comprehensive experiments show that our method outperforms the previous state-of-the-art, and quantitative analyses illustrate that our approach can alleviate the domain shift problem in out-of-distribution (OOD) scenarios

The Impact of Adding Online-to-Offline Service Platform Channels on Firms' Offline and Total Sales and Profits

Online-to-offline service platform (O2OSP) channels offer innovative means for customers to order local, daily services online (via apps) and have them delivered almost instantly offline. By comparing the business models underlying O2OSP, traditional online and offline, and platform based e-commerce channels, this article aims to identify the short- and long-term impacts of adding an O2OSP channel on firms' offline and total sales and profits. The analysis focuses primarily on a recent set of daily data gathered from a Chinese fast-food restaurant chain with 35 physical stores that also participates in four food delivery O2OSP channels. The panel data regressions with fixed effects reveal that adding O2OSP channels hurts offline and total profits in the short run but improves offline and total sales and profits in the long run. Specifically, offline and total sales increase by 23.28% and 33.94%, respectively. Thus, the O2OSP channel can serve as a complement to, rather than a substitute for, the offline channel. These results challenge previous research on the sales effects of adding (pure) online or offline channels and highlight the attractiveness of O2OSP channels for improving sales and profits. However, negative interaction effects among different O2OSP channels also signal that adding more O2OSP channels does not necessarily lead to profitable growth. (C) 2019 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved

Voltage Balancing Sorting Algorithm with Reduced Switching Frequency for Modular Multilevel Converters

PhD ThesisOver the last decade, Modular Multilevel Converters (MMCs) have been developed for medium- to high-voltage applications. They exhibit distinct features such as modularity, scalability, high degrees of redundancy and high-quality output voltage with the superior harmonic performance that reduces the requirement for filters. These features are unique to MMCs, thereby giving them a competitive advantage as an industrial solution over other voltage source multilevel converters. However, there are challenges associated with such converters when numerous submodules (SMs) are considered. The issues involved include voltage-balancing of the distributed SM, circulating current suppression, reliability, and increased complexity in the circuit configuration. The focus of this research is the voltage balancing of SMs. The most common and effective method of voltage-balancing is based on the well-known sorting algorithm, which results in higher switching frequency compared to other methods. This leads to substantially higher switching losses and hence lower efficiency, particularly when there are high numbers of SMs. Furthermore, the increased execution and calculation time leads to high computational complexity when the number of SM is high. This thesis proposes three new voltage balancing schemes to reduce the unnecessary switching events which are typically generated by the conventional sorting algorithm (CSA) and to reduce computational complexity: 1. The Index Selection Algorithm (ISA) is based on a constraint band of permissible voltage ripples and existing gate signals to offer three index options. This technique selects the optimum choice based on the number of SMs contained in the band. 2. The Hybrid Heap Sorting Algorithm (HSA) replaces the CSA with the heap sorting II algorithm. With this technique, the computational complexity is significantly decreased. 3. The Priority-based Sorting Algorithm (PSA) clusters the SMs of converter into different priority groups according to a pre-defined voltage ripple range along with the gate signal information of the previous sampling period. It helps to reduce the switching frequency by only selecting the necessary priority groups to be involved in the sorting stage. Another benefit of this scheme is its flexibility and great dynamic response to different pre-defined range. All the proposed algorithms produce fewer switching events and incur a lower computational cost, resulting in higher efficiency without detriment to the quality of the output waveform. The proposed voltage balancing schemes are tested using 4- and 22- level MMC models which were built using MATLAB/Simulink to investigate their performance. The converter performance is also validated for a small-scale 4-level MMC that was designed, built, and tested in the laboratory. The validation shows that the proposed algorithms clearly reduce the number of switching events. In addition, the algorithm can be easily incorporated without requiring hardware modifications

NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

Recent advances in neural implicit fields enables rapidly reconstructing 3D geometry from multi-view images. Beyond that, recovering physical properties such as material and illumination is essential for enabling more applications. This paper presents a new method that effectively learns relightable neural surface using pre-intergrated rendering, which simultaneously learns geometry, material and illumination within the neural implicit field. The key insight of our work is that these properties are closely related to each other, and optimizing them in a collaborative manner would lead to consistent improvements. Specifically, we propose NeuS-PIR, a method that factorizes the radiance field into a spatially varying material field and a differentiable environment cubemap, and jointly learns it with geometry represented by neural surface. Our experiments demonstrate that the proposed method outperforms the state-of-the-art method in both synthetic and real datasets

Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives

Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

A Simple Baseline for Supervised Surround-view Depth Estimation

Depth estimation has been widely studied and serves as the fundamental step of 3D perception for autonomous driving. Though significant progress has been made for monocular depth estimation in the past decades, these attempts are mainly conducted on the KITTI benchmark with only front-view cameras, which ignores the correlations across surround-view cameras. In this paper, we propose S3Depth, a Simple Baseline for Supervised Surround-view Depth Estimation, to jointly predict the depth maps across multiple surrounding cameras. Specifically, we employ a global-to-local feature extraction module which combines CNN with transformer layers for enriched representations. Further, the Adjacent-view Attention mechanism is proposed to enable the intra-view and inter-view feature propagation. The former is achieved by the self-attention module within each view, while the latter is realized by the adjacent attention module, which computes the attention across multi-cameras to exchange the multi-scale representations across surround-view feature maps. Extensive experiments show that our method achieves superior performance over existing state-of-the-art methods on both DDAD and nuScenes datasets

Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation

Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.Comment: AAAI2