Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation

Graph unlearning has emerged as a pivotal method to delete information from a pre-trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning method enables the GNN model to comply with data protection regulations (i.e., the right to be forgotten), adapt to evolving data distributions, and reduce the GPU-hours carbon footprint by avoiding repetitive retraining. Existing partitioning and aggregation-based methods have limitations due to their poor handling of local graph dependencies and additional overhead costs. More recently, GNNDelete offered a model-agnostic approach that alleviates some of these issues. Our work takes a novel approach to address these challenges in graph unlearning through knowledge distillation, as it distills to delete in GNN (D2DGN). It is a model-agnostic distillation framework where the complete graph knowledge is divided and marked for retention and deletion. It performs distillation with response-based soft targets and feature-based node embedding while minimizing KL divergence. The unlearned model effectively removes the influence of deleted graph elements while preserving knowledge about the retained graph elements. D2DGN surpasses the performance of existing methods when evaluated on various real-world graph datasets by up to $43.1\%$ (AUC) in edge and node unlearning tasks. Other notable advantages include better efficiency, better performance in removing target elements, preservation of performance for the retained elements, and zero overhead costs. Notably, our D2DGN surpasses the state-of-the-art GNNDelete in AUC by $2.4\%$, improves membership inference ratio by $+1.3$, requires $10.2\times10^6$ fewer FLOPs per forward pass and up to $\mathbf{3.2}\times$ faster

SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes

Detection of small-sized targets is of paramount importance in many aerial vision-based applications. The commonly deployed low cost unmanned aerial vehicles (UAVs) for aerial scene analysis are highly resource constrained in nature. In this paper we propose a simple short and shallow network (SSSDet) to robustly detect and classify small-sized vehicles in aerial scenes. The proposed SSSDet is up to 4x faster, requires 4.4x less FLOPs, has 30x less parameters, requires 31x less memory space and provides better accuracy in comparison to existing state-of-the-art detectors. Thus, it is more suitable for hardware implementation in real-time applications. We also created a new airborne image dataset (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of the proposed method is validated on the existing VEDAI, DLR-3K, DOTA and Combined dataset. The SSSDet outperforms state-of-the-art detectors in term of accuracy, speed, compute and memory efficiency.Comment: International Conference on Image Processing (ICIP) 2019, Taipei, Taiwa

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR)? The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesn't require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging

Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a single or multiple classes of data from an ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for the single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, doesn't put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. We will make the source code publicly available

Study on prediction of type 2 diabetes mellitus in undergraduate MBBS students: a cross-section study in a tertiary health center, Kolkata

Background: Diabetes, is now a leading cause of morbidity and mortality worldwide. Prevalence of type-2 diabetes in children and adolescents is rapidly increasing worldwide. Adolescence and early youth period has pivotal importance for young people with diabetes risk when they usually start learning about how to take responsibility. With this background, present study was done to find out the proportion of various risk factors and future risk of developing diabetes among MBBS undergraduates in Kolkata.Methods: This study was a cross-sectional institution based study done from 1st June to 18th June 2017. Data was collected by interviewing each respondent with the help of structured pre-designed pre-tested schedule, after which clinical examination for height, weight, waist and hip circumference, blood pressure and RBS were done. Out of 150 undergraduates, 130 agreed to participate. Data was analysed with R software.Results: Mean age of the students was 20.45years. 48.5% of them were either overweight or obese. Nearly half of them had waist circumference and waist hip ratio in risk group. Only 65 were normotensive and 7 had high random blood sugar (≥140 mg/dl). 6 students were in high risk group according to both IDRS (≥60) and ADA (≥5) risk score. 66 students were in moderate risk (30-50) group as per IDRS risk score.Conclusions: The simple and cost-effective IDRS could serve as a screening tool health worker to identify at risk individuals at the earliest and enable primary prevention by encouraging these students to modify their life-style