pwc-archive/papers-with-abstracts huggingface dataset:
CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation
Authors: Aanis Ahmad, Dharmendra Saraswat, Aly El Gamal, Gurmukh Johal
Date: 2021-10-22 00:00:00
Tasks: Management

Accurate disease identification and its severity estimation is an important consideration for disease management. Deep learning-based solutions for disease management using imagery datasets are being increasingly explored by the research community. However, most reported studies have relied on imagery datasets that were acquired under controlled lab conditions. As a result, such models lacked the ability to identify diseases in the field. Therefore, to train a robust deep learning model for field use, an imagery dataset was created using raw images acquired under field conditions using a handheld sensor and augmented images with varying backgrounds. The Corn Disease and Severity (CD&S) dataset consisted of 511, 524, and 562, field acquired raw images, corresponding to three common foliar corn diseases, namely Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS), respectively. For training disease identification models, half of the imagery data for each disease was annotated using bounding boxes and also used to generate 2343 additional images through augmentation using three different backgrounds. For severity estimation, an additional 515 raw images for NLS were acquired and categorized into severity classes ranging from 1 (resistant) to 5 (susceptible). Overall, the CD&S dataset consisted of 4455 total images comprising of 2112 field images and 2343 augmented images.
Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware
Authors: Jared Harris-Dewey, Richard Klein
Date: 2021-10-22 00:00:00
Tasks: GPU

We give an overview of the different rendering methods and we demonstrate that the use of a Generative Adversarial Networks (GAN) for Global Illumination (GI) gives a superior quality rendered image to that of a rasterisations image. We utilise the Pix2Pix architecture and specify the hyper-parameters and methodology used to mimic ray-traced images from a set of input features. We also demonstrate that the GANs quality is comparable to the quality of the ray-traced images, but is able to produce the image, at a fraction of the time.
A Simple Baseline for Low-Budget Active Learning
Authors: Kossar Pourahmadi, Parsa Nooralinejad, Hamed Pirsiavash
Date: 2021-10-22 00:00:00
Tasks: Active Learning, image-classification, Image Classification, Self-Supervised Learning

Active learning focuses on choosing a subset of unlabeled data to be labeled. However, most such methods assume that a large subset of the data can be annotated. We are interested in low-budget active learning where only a small subset (e.g., 0.2% of ImageNet) can be annotated. Instead of proposing a new query strategy to iteratively sample batches of unlabeled data given an initial pool, we learn rich features by an off-the-shelf self-supervised learning method only once, and then study the effectiveness of different sampling strategies given a low labeling budget on a variety of datasets including ImageNet. We show that although the state-of-the-art active learning methods work well given a large labeling budget, a simple K-means clustering algorithm can outperform them on low budgets. We believe this method can be used as a simple baseline for low-budget active learning on image classification. Code is available at: https://github.com/UCDvision/low-budget-al
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Authors: Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou
Date: 2021-10-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampling variability, class imbalance, and data-privacy concerns that often plague these methods, we instead provide a memory and computation-efficient probabilistic framework to extract class prototypes and align the target features with them. We demonstrate the general applicability of our method on a wide range of scenarios, including single-source, multi-source, class-imbalance, and source-private domain adaptation. Requiring no additional model parameters and having a moderate increase in computation over the source model alone, the proposed method achieves competitive performance with state-of-the-art methods.
Local-Global Associative Frame Assemble in Video Re-ID
Authors: Qilei Li, Jiabo Huang, Shaogang Gong
Date: 2021-10-22 00:00:00

Noisy and unrepresentative frames in automatically generated object bounding boxes from video sequences cause significant challenges in learning discriminative representations in video re-identification (Re-ID). Most existing methods tackle this problem by assessing the importance of video frames according to either their local part alignments or global appearance correlations separately. However, given the diverse and unknown sources of noise which usually co-exist in captured video data, existing methods have not been effective satisfactorily. In this work, we explore jointly both local alignments and global correlations with further consideration of their mutual promotion/reinforcement so to better assemble complementary discriminative Re-ID information within all the relevant frames in video tracklets. Specifically, we concurrently optimise a local aligned quality (LAQ) module that distinguishes the quality of each frame based on local alignments, and a global correlated quality (GCQ) module that estimates global appearance correlations. With the help of a local-assembled global appearance prototype, we associate LAQ and GCQ to exploit their mutual complement. Extensive experiments demonstrate the superiority of the proposed model against state-of-the-art methods on five Re-ID benchmarks, including MARS, Duke-Video, Duke-SI, iLIDS-VID, and PRID2011.
When to Prune? A Policy towards Early Structural Pruning
Authors: Maying Shen, Pavlo Molchanov, Hongxu Yin, Jose M. Alvarez
Date: 2021-10-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: GPU, Network Pruning

Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pruning, but suffers noticeable performance degradation. We attempt to combine the benefits of both directions and propose a policy that prunes as early as possible during training without hurting performance. Instead of pruning at initialization, our method exploits initial dense training for few epochs to quickly guide the architecture, while constantly evaluating dominant sub-networks via neuron importance ranking. This unveils dominant sub-networks whose structures turn stable, allowing conventional pruning to be pushed earlier into the training. To do this early, we further introduce an Early Pruning Indicator (EPI) that relies on sub-network architectural similarity and quickly triggers pruning when the sub-network's architecture stabilizes. Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise ``oracle'' grid-search that scans through epochs and requires orders of magnitude more compute. Our method yields $1.4\%$ top-1 accuracy boost over state-of-the-art pruning counterparts, cuts down training cost on GPU by $2.4\times$, hence offers a new efficiency-accuracy boundary for network pruning during training.
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations
Authors: Jinyong Hou, Xuejie Ding, Jeremiah D. Deng
Date: 2021-10-22 00:00:00
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

Semantic segmentation based on deep learning methods can attain appealing accuracy provided large amounts of annotated samples. However, it remains a challenging task when only limited labelled data are available, which is especially common in medical imaging. In this paper, we propose to use Leaking GAN, a GAN-based semi-supervised architecture for retina vessel semantic segmentation. Our key idea is to pollute the discriminator by leaking information from the generator. This leads to more moderate generations that benefit the training of GAN. As a result, the unlabelled examples can be better utilized to boost the learning of the discriminator, which eventually leads to stronger classification performance. In addition, to overcome the variations in medical images, the mean-teacher mechanism is utilized as an auxiliary regularization of the discriminator. Further, we modify the focal loss to fit it as the consistency objective for mean-teacher regularizer. Extensive experiments demonstrate that the Leaking GAN framework achieves competitive performance compared to the state-of-the-art methods when evaluated on benchmark datasets including DRIVE, STARE and CHASE\_DB1, using as few as 8 labelled images in the semi-supervised setting. It also outperforms existing algorithms on cross-domain segmentation tasks.
PhotoWCT$^2$: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals
Authors: Tai-Yin Chiu, Danna Gurari
Date: 2021-10-22 00:00:00
Tasks: 4k, Style Transfer

Photorealistic style transfer is an image editing task with the goal to modify an image to match the style of another image while ensuring the result looks like a real photograph. A limitation of existing models is that they have many parameters, which in turn prevents their use for larger image resolutions and leads to slower run-times. We introduce two mechanisms that enable our design of a more compact model that we call PhotoWCT$^2$, which preserves state-of-art stylization strength and photorealism. First, we introduce blockwise training to perform coarse-to-fine feature transformations that enable state-of-art stylization strength in a single autoencoder in place of the inefficient cascade of four autoencoders used in PhotoWCT. Second, we introduce skip connections of high-frequency residuals in order to preserve image quality when applying the sequential coarse-to-fine feature transformations. Our PhotoWCT$^2$ model requires fewer parameters (e.g., 30.3\% fewer) while supporting higher resolution images (e.g., 4K) and achieving faster stylization than existing models.
Spoofing Detection on Hand Images Using Quality Assessment
Authors: Asish Bera, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri, Hubert P. H. Shum
Date: 2021-10-22 00:00:00
Tasks: Classification

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification er ror using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples.
Classification of PS and ABS Black Plastics for WEEE Recycling Applications
Authors: Anton Persson, Niklas Dymne, Fernando Alonso-Fernandez
Date: 2021-10-20 00:00:00

Pollution and climate change are some of the biggest challenges that humanity is facing. In such a context, efficient recycling is a crucial tool for a sustainable future. This work is aimed at creating a system that can classify different types of plastics by using picture analysis, in particular, black plastics of the type Polystyrene (PS) and Acrylonitrile Butadiene Styrene (ABS). They are two common plastics from Waste from Electrical and Electronic Equipment (WEEE). For this purpose, a Convolutional Neural Network has been tested and retrained, obtaining a validation accuracy of 95%. Using a separate test set, average accuracy goes down to 86.6%, but a further look at the results shows that the ABS type is correctly classified 100% of the time, so it is the PS type that accumulates all the errors. Overall, this demonstrates the feasibility of classifying black plastics using CNN machine learning techniques. It is believed that if a more diverse and extensive image dataset becomes available, a system with higher reliability that generalizes well could be developed using the proposed methodology.
Detecting Wandering Behavior of People with Dementia
Authors: Nicklas Sindlev Andersen, Marco Chiarandini, Stefan Jänicke, Panagiotis Tampakis, Arthur Zimek
Date: 2021-10-25 00:00:00

Wandering is a problematic behavior in people with dementia that can lead to dangerous situations. To alleviate this problem we design an approach for the real-time automatic detection of wandering leading to getting lost. The approach relies on GPS data to determine frequent locations between which movement occurs and a step that transforms GPS data into geohash sequences. Those can be used to find frequent and normal movement patterns in historical data to then be able to determine whether a new on-going sequence is anomalous. We conduct experiments on synthetic data to test the ability of the approach to find frequent locations and to compare it against an alternative, state-of-the-art approach. Our approach is able to identify frequent locations and to obtain good performance (up to AUC = 0.99 for certain parameter settings) outperforming the state-of-the-art approach.
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
Authors: Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon
Date: 2021-10-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Management

We study the online problem of minimizing power consumption in systems with multiple power-saving states. During idle periods of unknown lengths, an algorithm has to choose between power-saving states of different energy consumption and wake-up costs. We develop a learning-augmented online algorithm that makes decisions based on (potentially inaccurate) predicted lengths of the idle periods. The algorithm's performance is near-optimal when predictions are accurate and degrades gracefully with increasing prediction error, with a worst-case guarantee almost identical to the optimal classical online algorithm for the problem. A key ingredient in our approach is a new algorithm for the online ski rental problem in the learning augmented setting with tight dependence on the prediction error. We support our theoretical findings with experiments.
Gophormer: Ego-Graph Transformer for Node Classification
Authors: Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye
Date: 2021-10-25 00:00:00
Tasks: Classification, Data Augmentation, Graph Mining, Graph Neural Network, Graph Sampling, Node Classification

Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area, where graph neural network (GNN) has been the dominant paradigm, transformers haven't achieved competitive performance, especially on the node classification task. Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases. To alleviate these issues, we propose a novel Gophormer model which applies transformers on ego-graphs instead of full-graphs. Specifically, Node2Seq module is proposed to sample ego-graphs as the input of transformers, which alleviates the challenge of scalability and serves as an effective data augmentation technique to boost model performance. Moreover, different from the feature-based attention strategy in vanilla transformers, we propose a proximity-enhanced attention mechanism to capture the fine-grained structural bias. In order to handle the uncertainty introduced by the ego-graph sampling, we further propose a consistency regularization and a multi-sample inference strategy for stabilized training and testing, respectively. Extensive experiments on six benchmark datasets are conducted to demonstrate the superiority of Gophormer over existing graph transformers and popular GNNs, revealing the promising future of graph transformers.
Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints
Authors: Yanlin Chen, Ronald de Wolf
Date: 2021-10-25 00:00:00
Tasks: regression

Lasso and Ridge are important minimization problems in machine learning and statistics. They are versions of linear regression with squared loss where the vector $\theta\in\mathbb{R}^d$ of coefficients is constrained in either $\ell_1$-norm (for Lasso) or in $\ell_2$-norm (for Ridge). We study the complexity of quantum algorithms for finding $\varepsilon$-minimizers for these minimization problems. We show that for Lasso we can get a quadratic quantum speedup in terms of $d$ by speeding up the cost-per-iteration of the Frank-Wolfe algorithm, while for Ridge the best quantum algorithms are linear in $d$, as are the best classical algorithms. As a byproduct of our quantum lower bound for Lasso, we also prove the first classical lower bound for Lasso that is tight up to polylog-factors.
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks
Authors: Giacomo Arcieri, David Wölfle, Eleni Chatzi
Date: 2021-10-25 00:00:00
Tasks: Benchmarking, continuous-control, Continuous Control, Gaussian Processes, Reinforcement Learning (RL)

The need for algorithms able to solve Reinforcement Learning (RL) problems with few trials has motivated the advent of model-based RL methods. The reported performance of model-based algorithms has dramatically increased within recent years. However, it is not clear how much of the recent progress is due to improved algorithms or due to improved models. While different modeling options are available to choose from when applying a model-based approach, the distinguishing traits and particular strengths of different models are not clear. The main contribution of this work lies precisely in assessing the model influence on the performance of RL algorithms. A set of commonly adopted models is established for the purpose of model comparison. These include Neural Networks (NNs), ensembles of NNs, two different approximations of Bayesian NNs (BNNs), that is, the Concrete Dropout NN and the Anchored Ensembling, and Gaussian Processes (GPs). The model comparison is evaluated on a suite of continuous control benchmarking tasks. Our results reveal that significant differences in model performance do exist. The Concrete Dropout NN reports persistently superior performance. We summarize these differences for the benefit of the modeler and suggest that the model choice is tailored to the standards required by each specific application.
Unsupervised Source Separation By Steering Pretrained Music Models
Authors: Ethan Manilow, Patrick O'Reilly, Prem Seetharaman, Bryan Pardo
Date: 2021-10-25 00:00:00
Tasks: Audio Generation, Audio Source Separation, Music Generation, Music Tagging, TAG

We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generation model is conditioned on an input mixture, producing a latent encoding of the audio used to generate audio. This generated audio is fed to a pretrained music tagger that creates source labels. The cross-entropy loss between the tag distribution for the generated audio and a predefined distribution for an isolated source is used to guide gradient ascent in the (unchanging) latent space of the generative model. This system does not update the weights of the generative model or the tagger, and only relies on moving through the generative model's latent space to produce separated sources. We use OpenAI's Jukebox as the pretrained generative model, and we couple it with four kinds of pretrained music taggers (two architectures and two tagging datasets). Experimental results on two source separation datasets, show this approach can produce separation estimates for a wider variety of sources than any tested supervised or unsupervised system. This work points to the vast and heretofore untapped potential of large pretrained music models for audio-to-audio tasks like source separation.
Evolutionary Optimization of High-Coverage Budgeted Classifiers
Authors: Nolan H. Hamilton, Errin W. Fulp
Date: 2021-10-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Classifiers are often utilized in time-constrained settings where labels must be assigned to inputs quickly. To address these scenarios, budgeted multi-stage classifiers (MSC) process inputs through a sequence of partial feature acquisition and evaluation steps with early-exit options until a confident prediction can be made. This allows for fast evaluation that can prevent expensive, unnecessary feature acquisition in time-critical instances. However, performance of MSCs is highly sensitive to several design aspects -- making optimization of these systems an important but difficult problem. To approximate an initially intractable combinatorial problem, current approaches to MSC configuration rely on well-behaved surrogate loss functions accounting for two primary objectives (processing cost, error). These approaches have proven useful in many scenarios but are limited by analytic constraints (convexity, smoothness, etc.) and do not manage additional performance objectives. Notably, such methods do not explicitly account for an important aspect of real-time detection systems -- the ratio of "accepted" predictions satisfying some confidence criterion imposed by a risk-averse monitor. This paper proposes a problem-specific genetic algorithm, EMSCO, that incorporates a terminal reject option for indecisive predictions and treats MSC design as an evolutionary optimization problem with distinct objectives (accuracy, cost, coverage). The algorithm's design emphasizes Pareto efficiency while respecting a notion of aggregated performance via a unique scalarization. Experiments are conducted to demonstrate EMSCO's ability to find global optima in a variety of Theta(k^n) solution spaces, and multiple experiments show EMSCO is competitive with alternative budgeted approaches.