pwc-archive/papers-with-abstracts huggingface dataset:
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks
Authors: Yair Zohav, Shahaf E Finder, Maor Ashkenazi, Eran Treister
Date: 2021-09-29 00:00:00
Tasks: Depth Estimation, Depth Prediction, Image Compression, Quantization, Semantic Segmentation

Quantization is one of the most effective techniques for compressing Convolutional Neural Networks (CNNs), which are known for requiring extensive computational resources. However, aggressive quantization may cause severe degradation in the prediction accuracy of such networks, especially in image-to-image tasks such as semantic segmentation and depth prediction. In this paper, we propose Wavelet Compressed Convolution (WCC)---a novel approach for activation maps compression for $1\times1$ convolutions (the workhorse of modern CNNs). WCC achieves compression ratios and computational savings that are equivalent to low bit quantization rates at a relatively minimal loss of accuracy. To this end, we use a hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and define the convolution on the compressed activation map. WCC can be utilized with any $1\times1$ convolution in an existing network architecture. By combining WCC with light quantization, we show that we achieve compression rates equal to 2-bit and 1-bit with minimal degradation in image-to-image tasks.
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images
Authors: Stefano Samele, Matteo Matteucci
Date: 2021-09-29 00:00:00
Tasks: Anomaly Detection, Dictionary Learning, Representation Learning

In this work, we investigate a methodology to perform anomaly detection and localization on images. The method leverages both sparse representation learning and the adoption of a pre-trained neural network for classification purposes. The objective is to assess the effectiveness of the K-SVD sparse dictionary learning algorithm and understand the role of neural network activation maps as data descriptors. We extract meaningful representation features and build a sparse dictionary of the most expressive ones. The dictionary is built only over features coming from images without anomalies. Thus, images containing anomalies will either have a non-sparse representation as linear combinations of the dictionary elements or a high reconstruction error. We show that the proposed pipeline achieves state-of-the-art performance in terms of AUC-ROC score over benchmarks such as MVTec Anomaly Detection, Rd-MVTec Anomaly Detection, Magnetic Tiles Defect, and BeanTech Anomaly Detection Datasets.
SLIM-QN: A Stochastic, Light, Momentumized Quasi-Newton Optimizer for Deep Neural Networks
Authors: Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr
Date: 2021-09-29 00:00:00
Tasks: Second-order methods

We propose SLIM-QN, a light stochastic quasi-Newton optimizer for training large-scale deep neural networks (DNNs).
SLIM-QN addresses two key barriers in existing second-order methods for large-scale DNNs: 1) the high computational cost of obtaining the Hessian matrix and its inverse in every iteration (e.g. KFAC); 2) convergence instability due to stochastic training (e.g. L-BFGS).
To tackle the first challenge,SLIM-QN directly approximates the Hessian inverse using past parameters and gradients, without explicitly constructing the Hessian matrix and then computing its inverse.
To achieve stable convergence, SLIM-QN introduces momentum in Hessian updates together with an adaptive damping mechanism.
We provide rigorous theoretical results on the convergence of SLIM-QN in a stochastic setting.
We also demonstrate that SLIM-QN has much less compute and memory overhead compared to existing second-order methods. 
To better understand the limitations and benefits of SLIM-QN, we evaluate its performance on various datasets and network architectures.
For instance on large datasets such as ImageNet, we show that SLIM-QN achieves near optimal accuracy $1.5\times$ faster when compared with SGD ($1.36\times$ faster in wall-clock time) using the same compute resources.
We also show that SLIM-QN can readily be applied to other contemporary non-convolutional architectures such as Transformers.
DL-based prediction of optimal actions of human experts
Authors: Jung H. Lee, Ryan S Butner, Elise Saxon, Nathan Oken Hodas
Date: 2021-09-29 00:00:00
Tasks: Decision Making

Expert systems have been developed to emulate human experts’ decision-making. Once developed properly, expert systems can assist or substitute human experts, but they require overly expensive knowledge engineering/acquisition. Notably, deep learning (DL) can train highly efficient computer vision systems only from examples instead of relying on carefully selected feature sets by human experts. Thus, we hypothesize that DL can be used to build expert systems that can learn human experts’ decision-making from examples only without relying on overly expensive knowledge engineering. To address this hypothesis, we train DL agents to predict optimal strategies (actions or action sequences) for the popular game `Angry Birds’, which requires complex problem-solving skills. In our experiments, after being trained with screenshots of different levels and pertinent 3-star guides, DL agents can predict strategies for unseen levels. This raises the possibility of building DL-based expert systems that do not require overly expensive knowledge engineering.
Explaining Scaling Laws of Neural Network Generalization
Authors: Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma
Date: 2021-09-29 00:00:00

The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents: super-classing image tasks does not change exponents, while changing input distribution (via changing datasets or adding noise) has a strong effect. We further explore the effect of architecture aspect ratio on scaling exponents.
Decomposing Texture and Semantics for Out-of-distribution Detection
Authors: Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
Date: 2021-09-29 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Philosophy

Out-of-distribution (OOD) detection has made significant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system.Nevertheless, the lack of precise interpretation of the in-distribution limits the application of OOD detection methods to real-world system pipielines. To tackle this issue, we decompose the definition of the in-distribution into texture and semantics, motivated by real-world scenarios. In addition, we design new benchmarks to measure the robustness that OOD detection methods should have. To achieve a good balance between the OOD detection performance and robustness, our method takes a divide-and-conquer approach. That is, the model first tackles each component of the texture and semantics separately, and then combines them later. Such design philosophy is empirically proven by a series of benchmarks including not only ours but also the conventional counterpart.
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS
Authors: Mononito Goswami, Chufan Gao, Benedikt Boecking, Saswati Ray, Artur Dubrawski
Date: 2021-09-29 00:00:00
Tasks: Active Learning

Supervised machine learning (ML) has fueled major advances in several domains such as health, education and governance. However, most modern ML methods rely on vast quantities of point-by-point hand-labeled training data. In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms. Data programming, a framework for learning from weak supervision, attempts to overcome this bottleneck by generating probabilistic training labels from simple yet imperfect heuristics obtained a priori from domain experts. We present WARM, Active Refinement of Weakly Supervised Models, a principled approach to iterative and interactive improvement of weakly supervised models via active learning. WARM directs domain experts' attention on a few selected data points that, when annotated, would improve the label model's probabilistic output in terms of accuracy the most. Gradient backpropagation is then used to iteratively update decision parameters of the heuristics of the label model. Experiments on multiple real-world medical classification datasets reveal that WARM can substantially improve the accuracy of probabilistic labels, a direct measure of training data quality, with as few as 30 queries to clinicians. Additional experiments with domain shift and artificial noise in the LFs, demonstrate WARM's ability to adapt heuristics and the end model to changing population characteristics as well as its robustness to mis-specification of domain-expert-acquired LFs. These capabilities make WARM a potentially useful tool for deploying, maintaining, and auditing weakly supervised systems in practice.
DaSeGAN: Domain Adaptation for Segmentation Tasks via Generative Adversarial Networks
Authors: Mario Parreño Lara, Roberto Paredes, Alberto Albiol
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Image Segmentation, Multi-Source Unsupervised Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

A weakness of deep learning methods is that they can fail when there is a mismatch between source and target data domains. In medical image applications, this is a common situation when data from new vendor devices or different hospitals is available. Domain adaptation techniques aim to fill this gap by generating mappings between image domains when unlabeled data from the new target domain is available. In other cases, no target domain data (labeled or unlabeled) is available during training. In this latter case, domain generalization methods focus on learning domain-invariant representations which are more robust to new domains. In this paper, a combination of domain adaptation and generalization techniques is proposed by leveraging domain-invariant image translations for image segmentation problems. This is achieved by adversarially training a generator that transforms source images to a universal domain. To preserve the semantic consistency between the source and universal domains a segmentation consistency loss between the source and universal predictions is used. Our method was validated on the M&Ms dataset, a multi-source unsupervised domain adaptation, and generalization problem, outperforming previous methods. Particularly, our method significantly boosts the test results over the unlabeled and unseen domains without hurting source-labeled domains.
Feature Grinding: Efficient Backdoor Sanitation in Deep Neural Networks
Authors: Nils Lukas, Charles Zhang, Florian Kerschbaum
Date: 2021-09-29 00:00:00
Tasks: Backdoor Attack

Training deep neural networks (DNNs) is expensive and for this reason, third parties provide computational resources to train models. This makes DNNs vulnerable to backdoor attacks, in which the third party maliciously injects hidden functionalities in the model at training time. Removing a backdoor is challenging because although the defender has access to a clean, labeled dataset, they only have limited computational resources which are a fraction of the resources required to train a model from scratch. We propose Feature Grinding as an efficient, randomized backdoor sanitation technique against seven contemporary backdoors on CIFAR-10 and ImageNet. Feature Grinding requires at most six percent of the model's training time on CIFAR-10 and at most two percent on ImageNet for sanitizing the surveyed backdoors. We compare Feature Grinding with five other sanitation methods and find that it is often the most effective at decreasing the backdoor's success rate while preserving a high model accuracy. Our experiments include an ablation study over multiple parameters for each backdoor attack and sanitation technique to ensure a fair evaluation of all methods. Models suspected of containing a backdoor can be Feature Grinded using limited resources, which makes it a practical defense against backdoors that can be incorporated into any standard training procedure.
Fourier Features in Reinforcement Learning with Neural Networks
Authors: David Brellmann, Goran Frehse, David Filliat
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In classic Reinforcement Learning (RL),  encoding the inputs with a Fourier feature mapping is a standard way to facilitate generalization and add prior domain knowledge. In Deep RL, such input encodings are less common, since they could, in principle, be learned by the network and may therefore seem less beneficial. In this paper, we present experiments on Multilayer Perceptron (MLP) that indicate that even in Deep RL, Fourier features can lead to significant performance gains, in both rewards and sample efficiency. Furthermore, we observe that they increase the robustness with respect to hyperparameters, lead to smoother policies, and benefit the training process by reducing learning interference, encouraging sparsity, and increasing the expressiveness of the learned features. According to our experiments, other input preprocessings, such as random Fourier features or Polynomial features, do not give similar advantages.But a major bottleneck with conventional Fourier features is that they exponentially increase the number of features with the state dimension. We remedy this by proposing a simple, light version that only has a linear number of features, yet still maintains the benefits. Our experiments cover both shallow/deep, discrete/continuous, and on/off-policy RL settings. To the best of our knowledge, this is the first reported application of Fourier features in Deep RL.
Adaptive Unbiased Teacher for Cross-Domain Object Detection
Authors: Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris M. Kitani, Peter Vajda
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Object, object-detection, Object Detection

We tackle the problem of domain adaptation in object detection, where the main challenge lies in significant domain shifts between source (one domain with supervision) and target (a domain of interest without supervision).   Although the teacher-student framework (a student model learns from pseudo labels generated from a teacher model) has been adopted to enable domain adaptation and yielded accuracy  gains  on  the  target  domain,  the  teacher  model  still  generates  a  large number of low-quality pseudo labels (e.g.,false positives) due to its bias toward source domain. This leads to sub-optimal domain adaptation performance. To ad-dress this issue, we propose Adaptive Unbiased Teacher (AUT), a teacher-student framework leveraging adversarial learning (on features derived from backbone)and weak-strong data augmentation to address domain shifts. Specifically, we em-ploy feature-level adversarial training, ensuring features extracted from the source and target domains share similar statistics. This enables the student model to capture domain-invariant features. Furthermore, we apply weak-strong augmentation and mutual learning of the teacher for target domain and student model for both domains.  This enables the updated teacher model to gradually benefit from the student model without suffering domain shift.  We show that AUT demonstrates superiority over all existing approaches and even Oracle (fully-supervised) mod-els by a huge margin.  For example, we achieve 50.9% (49.3%) mAP on FoggyCityscape (Clipart1K), which is 9.2% (5.2%) and 8.2% (11.0%) higher than previous state of the arts and Oracle, respectively.
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Authors: Chongchong Li, Yue Wang, Wei Chen, YuTing Liu, Zhi-Ming Ma, Tie-Yan Liu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: continuous-control, Continuous Control, Model-based Reinforcement Learning

Model-based reinforcement learning provides an efficient mechanism to find the optimal policy by interacting with the learned environment. In addition to treating the learned environment like a black-box simulator, a more effective way to use the model is to exploit its differentiability. Such methods require the gradient information of the learned environment model when calculating the policy gradient. However, since the error of gradient is not considered in the model learning phase, there is no guarantee for the model's accuracy. To address this problem, we first analyze the convergence rate for the policy optimization methods when the policy gradient is calculated using the learned environment model. The theoretical results show that the model gradient error matters in the policy optimization phrase. Then we proposed a two-model-based learning method to control the prediction error and the gradient error. We separate the different roles of these two models at the model learning phase and coordinate them at the policy optimization phase. After proposed the method, we introduce the directional derivative projection policy optimization (DDPPO) algorithm as a piratical implementation to find the optimal policy. Finally, We empirically verify the effectiveness of the proposed algorithm and yield the state-of-the-art performance on sample efficiency through benchmark continuous control tasks.
Causally Focused Convolutional Networks Through Minimal Human Guidance
Authors: Rimmon Saloman Bhosale, Mrinal Das
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Networks (CNNs) are the state of the art in image classification mainly due to their ability to automatically extract features from the images and in turn, achieve accuracy higher than any method in history. However, the flip side is, they are correlational models which aggressively learn features that highly correlate with the labels. Such features may not be causally related to the labels as per human cognition. For example, in a subset of images, cows can be on grassland, but classifying an image as cow based on the presence of grassland is incorrect. To marginalize out the effect of all possible contextual features we need to gather a huge training dataset, which is not always possible. Moreover, this prohibits the model to justify the decision. This issue has some serious implications in certain domains such as medicine, where the amount of data can be limited but the model is expected to justify its decisions. In order to mitigate this issue, our proposal is to focus CNN to extract features that are causal from a human perspective. We propose a mechanism to accept guidance from humans in the form of activation masks to modify the learning process of CNN. The amount of additional guidance can be small and can be easily formed. Through detailed analysis, we show that this method not only improves the learning of causal features but also helps in learning efficiently with less data. We demonstrate the effectiveness of our method against multiple datasets using quantitative as well as qualitative results.
Momentum as Variance-Reduced Stochastic Gradient
Authors: Zih-Syuan Huang, Ching-pei Lee
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation

Stochastic gradient descent with momentum (SGD+M) is widely used to empirically improve the convergence behavior and the generalization performance of plain stochastic gradient descent (SGD) in the training of deep learning models, but our theoretical understanding for SGD+M is still very limited.  Contrary to the conventional wisdom that sees the momentum in SGD+M as a way to extrapolate the iterates, this work provides an alternative view that interprets the momentum in SGD+M as a (biased) variance-reduced stochastic gradient.  We rigorously prove that the momentum in SGD+M converges to the real gradient, with the variance vanishing asymptotically.  This reduced variance in gradient estimation thus provides better convergence behavior and opens up a different path for future analyses of momentum methods.  Because the reduction of the variance in the momentum requires neither a finite-sum structure in the objective function nor complicated hyperparameters to tune, SGD+M works on complicated deep learning models with possible involvement of data augmentation and dropout, on which many other variance reduction methods fail.
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation
Authors: Gleb Yengalych, Igor E. Kuralenok, Vasily A Ershov
Date: 2021-09-29 00:00:00
Tasks: Out of Distribution (OOD) Detection

Uncertainty estimation and out-of-doman (OOD) input detection are critical for improving the safety and robustness of machine learning. Unfortunately, most methods for detecting OOD examples have been evaluated on small tasks while typical methods are computationally expensive. In this paper we propose a new gradient-based method called GRODIN for OOD detection. The proposed method is conceptually simple, computationally cheaper than ensemble methods and can be directly applied to any existing and deployed model without re-training. We evaluate GRODIN on models trained on CIFAR-10 and ImageNet datasets, and show it's strong performance on various OOD ImageNet datasets such as ImageNet-O, ImageNet-A, ImageNet-R, ImageNet-C.
Deep Reinforcement Learning for Equal Risk Option Pricing and Hedging under Dynamic Expectile Risk Measures
Authors: Saeed Marzban, Erick Delage, Jonathan Li
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider coherent risk measures. However, all current implementations either employ a static risk measure or are based on traditional dynamic programming solution schemes that are impracticable in realistic settings: when the number of underlying assets is large  or only historical trajectories are available. This paper extends for the first time the deep deterministic policy gradient algorithm to the problem of solving a risk averse Markov decision process that models risk using a time consistent dynamic expectile risk measure. Our numerical experiments, which involve both a simple vanilla option and a more exotic basket option, confirm that the new ACRL algorithm can produce high quality hedging strategies that produce accurate prices in simple settings, and outperform the strategies produced using static risk measures when the risk is evaluated at later points of time.