Heterogeneous Forgetting Compensation for Class-Incremental Learning

Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all old categories have the same forgetting pace, and neglect negative influence of forgetting heterogeneity among different old classes on forgetting compensation. To surmount the above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous forgetting of easy-to-forget and hard-to-forget old categories from both representation and gradient aspects. Specifically, we design a task-semantic aggregation block to alleviate heterogeneous forgetting from representation aspect. It aggregates local category information within each task to learn task-shared global representations. Moreover, we develop two novel plug-and-play losses: a gradient-balanced forgetting compensation loss and a gradient-balanced relation distillation loss to alleviate forgetting from gradient aspect. They consider gradient-balanced compensation to rectify forgetting heterogeneity of old categories and heterogeneous relation consistency. Experiments on several representative datasets illustrate effectiveness of our HFC model. The code is available at https://github.com/JiahuaDong/HFC.Comment: Accepted to ICCV202

Mining and Predicting Smart Device User Behavior

Three types of user behavior are mined in this paper: application usage, smart device usage and periodicity of user behavior. When mining application usage, the application installation, most frequently used applications and application correlation are analyzed. The application usage is long-tailed. When mining the device usage, the mean, variance and autocorrelation are calculated both for duration and interval. Both the duration and interval are long-tailed but only duration satisfies power-law distribution. Meanwhile, the autocorrelation of both duration and interval is weak, which makes predicting user behavior based on adjacent behavior not so reasonable in related works. Then DFT (Discrete Fourier Transform) is utilized to analyze the periodicity of user behavior and results show that the most obvious periodicity is 24 hours, which is in agreement with related works. Based on the results above, an improved user behavior predicting model is proposed based on Chebyshev inequality. Experiment results show that the performance is good in accurate rate and recall rate

Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection

Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector

Nuclear-receptor–mediated regulation of drug– and bile-acid–transporter proteins in gut and liver

This is an Accepted Manuscript of an article published by Taylor & Francis in Drug Metabolism Reviews on 2015 Sep 2, available online: http://www.tandfonline.com/10.3109/03602532.2012.748793.Adverse drug events (ADEs) are a common cause of patient morbidity and mortality and are classically thought to result, in part, from variation in expression and activity of hepatic enzymes of drug metabolism. It is now known that alterations in the expression of genes that encode drug- and bile-acid–transporter proteins in both the gut and liver play a previously unrecognized role in determining patient drug response and eventual clinical outcome. Four nuclear receptor (NR) superfamily members, including pregnane X receptor (PXR, NR1I2), constitutive androstane receptor (NR1I3), farnesoid X receptor (NR1H4), and vitamin D receptor (NR1I1), play pivotal roles in drug- and bile-acid– activated programs of gene expression to coordinately regulate drug- and bile-acid transport activity in the intestine and liver. This review focuses on the NR-mediated gene activation of drug and bile-acid transporters in these tissues as well as the possible underlying molecular mechanisms

Create Your World: Lifelong Text-to-Image Diffusion

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a use's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://wenqiliang.github.io/.Comment: 15 pages,10 figure