CellTradeMap: Delineating trade areas for urban commercial districts with cellular networks

Understanding customer mobility patterns to com-mercial districts is crucial for urban planning, facility manage-ment, and business strategies. Trade areas are a widely appliedmeasure to quantify where the visitors are from. Traditionaltrade area analysis is limited to small-scale or store-level studiesbecause information such as visits to competitor commercialentities and place of residence is collected by labour-intensivequestionnaires or heavily biased location-based social media data.In this paper, we propose CellTradeMap, a novel district-leveltrade area analysis framework using mobile flow records (MFRs),a type of fine-grained cellular network data. CellTradeMap ex-tracts robust location information from the irregularly sampled,noisy MFRs, adapts the generic trade area analysis frameworkto incorporate cellular data, and enhances the original trade areamodel with cellular-based features. We evaluate CellTradeMap ona large-scale cellular network dataset covering 3.5 million mobilephone users in a metropolis in China. Experimental results showthat the trade areas extracted by CellTradeMap are aligned withdomain knowledge and CellTradeMap can model trade areaswith a high predictive accuracy

CLR: Channel-wise Lightweight Reprogramming for Continual Learning

Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned tasks after learning new tasks, i.e., to avoid catastrophic forgetting. We propose a Channel-wise Lightweight Reprogramming (CLR) approach that helps convolutional neural networks (CNNs) overcome catastrophic forgetting during continual learning. We show that a CNN model trained on an old task (or self-supervised proxy task) could be ``reprogrammed" to solve a new task by using our proposed lightweight (very cheap) reprogramming parameter. With the help of CLR, we have a better stability-plasticity trade-off to solve continual learning problems: To maintain stability and retain previous task ability, we use a common task-agnostic immutable part as the shared ``anchor" parameter set. We then add task-specific lightweight reprogramming parameters to reinterpret the outputs of the immutable parts, to enable plasticity and integrate new knowledge. To learn sequential tasks, we only train the lightweight reprogramming parameters to learn each new task. Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting. To minimize the parameter requirement of reprogramming to learn new tasks, we make reprogramming lightweight by only adjusting essential kernels and learning channel-wise linear mappings from anchor parameters to task-specific domain knowledge. We show that, for general CNNs, the CLR parameter increase is less than 0.6\% for any new task. Our method outperforms 13 state-of-the-art continual learning baselines on a new challenging sequence of 53 image classification datasets. Code and data are available at https://github.com/gyhandy/Channel-wise-Lightweight-ReprogrammingComment: ICCV 202

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-AnythingComment: 16 pages (including appendix and references), 3 figure