pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation
Authors: Dennis Thomas Ulmer
Date: 2021-09-29 00:00:00
Tasks: Survey

Popular approaches for quantifying predictive uncertainty in deep neural networks often involve a set of weights or models, for instance via ensembling or Monte Carlo Dropout. These techniques usually produce overhead by having to train multiple model instances or do not produce very diverse predictions. This survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they admit “what they don’t know” and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting. Finally, we survey the application of the same paradigm to regression problems. We also provide a reflection on the strengths and weaknesses of the mentioned approaches compared to existing ones and provide the most central theoretical results in order to inform future research.
Self-Supervised Structured Representations for Deep Reinforcement Learning
Authors: Hyesong Choi, Hunsang Lee, Wonil Song, Sangryul Jeon, Kwanghoon Sohn, Dongbo Min
Date: 2021-09-29 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Image Reconstruction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Supervised Learning

Recent reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies. However, these methods focus on learning global representations of images, and disregard local spatial structures present in the consecutively stacked frames. In this paper, we propose a novel approach that learns self-supervised structured representations ($\mathbf{S}^3$R) for effectively encoding such spatial structures in an unsupervised manner. Given the input frames, the structured latent volumes are first generated individually using an encoder, and they are used to capture the change in terms of spatial structures, i.e., flow maps among multiple frames. To be specific, the proposed method establishes flow vectors between two latent volumes via a supervision by the image reconstruction loss. This enables for providing plenty of local samples for training the encoder of deep RL. We further attempt to leverage the structured representations in the self-predictive representations (SPR) method that predicts future representations using the action-conditioned transition model. The proposed method imposes similarity constraints on the three latent volumes; warped query representations by estimated flows, predicted target representations from the transition model, and target representations of future state. Experimental results on complex tasks in Atari Games and DeepMind Control Suite demonstrate that the RL methods are significantly boosted by the proposed self-supervised learning of structured representations.
The code is available at https://sites.google.com/view/iclr2022-s3r.
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification
Authors: Wang Lu, Jindong Wang, Yiqiang Chen, Xinwei Sun
Date: 2021-09-29 00:00:00
Tasks: Activity Recognition, Classification, Gesture Recognition, Human Activity Recognition, Time Series, Time Series Analysis, Time Series Classification

Time series classification is an important problem in real world. Due to its nonstationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view the time series classification problem from the distribution perspective. We argue that the temporal complexity attributes to the unknown latent distributions within. To this end, we propose DIVERSIFY to learn generalized representations for time series classification. DIVERSIFY takes an iterative process: it first obtains the worst-case distribution scenario via adversarial training, then matches the distributions between all segments. We also present some theoretical insights. Extensive experiments on gesture recognition, speech commands recognition, and sensor-based human activity recognition demonstrate that DIVERSIFY significantly outperforms other baselines while effectively characterizing the latent distributions by qualitative and quantitative analysis.
A Multi-Task Learning Algorithm for Non-personalized Recommendations
Authors: Jiawei Zhang
Date: 2021-09-29 00:00:00
Tasks: Collaborative Filtering, Multi-Task Learning, Recommendation Systems

In this paper, we introduce a multi-task learning (MTL) algorithm for recommending non-personalized videos to watch next on industrial video sharing platforms. Personalized recommendations have been studied for decades, while researches on non-personalized solutions are very rare to be seen, which still remain a huge portion in industry. As an indispensable part in recommender system, non-personalized video recommender system also faces several real-world challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on recommendation quality.
Adaptive Region Pooling for Fine-Grained Representation Learning
Authors: Tsai-Shien Chen, Chih-Ting Liu, Shao-Yi Chien
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification, Image Retrieval, Representation Learning, Retrieval

Fine-grained recognition aims to discriminate the sub-categories of the images within one general category. It is fundamentally difficult due to the requirement to extract fine-grained features from subtle regions. Nonetheless, a Convolutional Neural Network typically applies strided operations to downsample the representation, which would excessively spoil the feature resolution and lead to a significant loss of fine-grained information. In this paper, we propose Adaptive Region Pooling (ARP): a novel downsampling algorithm that makes the network only focus on a smaller but more critical region, and simultaneously increase the resolution of sub-sampled feature. ARP owns a trade-off mechanism that allows users to actively balance the scale of receptive field and the granularity of feature. Also, without any learning-based parameters, ARP provides the network a stabler training process and an earlier convergence. Extensive experiments qualitatively and quantitatively validate the effectiveness and efficiency of the proposed pooling operation and show superior performance against the state-of-the-arts in both the tasks of image classification and image retrieval.
OUMG: Objective and Universal Metric for Text Generation with Guiding Ability
Authors: Hanxu Liu, Nianmin Yao
Date: 2021-09-29 00:00:00
Tasks: Sentence, Story Generation, Text Generation

Existing evaluation metrics for text generation rely on comparing candidate sentences to reference sentences. Some text generation tasks, such as story generation and poetry generation, have no fixed optimal answer and cannot match a corresponding reference for each sentence. Therefore, there is a lack of an objective and universal evaluation metric. To this end, we propose OUMG, a general metric that does not depend on reference standards. We train a discriminator to distinguish between human-generated and machine-generated text, which is used to score the sentences generated by the model. These scores reflect how similar the sentences are to human-generated texts. The capability of the discriminator can be measured by its accuracy, so it avoids the subjectivity of human judgments. Furthermore, the trained discriminator can also guide the text generation process to improve model performance. Experiments on poetry generation demonstrate that OUMG can objectively evaluate text generation models without reference standards. After combining the discriminator with the generation model, the original model can produce significantly higher quality results.
Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations
Authors: Sohei Arisaka, Qianxiao Li
Date: 2021-09-29 00:00:00
Tasks: Meta-Learning

In science and engineering applications, it is often required to solve similar computational problems repeatedly. In such cases, we can utilize the data from previously solved problem instances to improve efficiency of finding subsequent solutions. This offers a unique opportunity to combine machine learning (in particular, meta-learning) and scientific computing. To date, a variety of such domain-specific methods have been proposed in the literature, but a generic approach for designing these methods remains under-explored. In this paper, we tackle this issue by formulating a general framework to describe these problems, and propose a gradient-based algorithm to solve them in a unified way. As an illustration of this approach, we study the adaptive generation of initial guesses for iterative solvers to speed up the solution of differential equations. We demonstrate the performance and versatility of our method through theoretical analysis and numerical experiments.
Synthesising Audio Adversarial Examples for Automatic Speech Recognition
Authors: Xinghua Qu, Pengfei Wei, Mingyong Gao, Zhu Sun, Yew-Soon Ong, Zejun Ma
Date: 2021-09-29 00:00:00
Tasks: Audio Synthesis, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly. Existing audio adversarial examples are typically constructed by adding constrained perturbations on benign audio inputs. Such attacks are therefore generated with an audio dependent assumption. For the first time, we propose the Speech Synthesising based Attack (SSA), a novel threat model that constructs audio adversarial examples entirely from scratch, i.e., without depending on any existing audio) to fool cutting-edge ASR models. To this end, we introduce a conditional variational auto-encoder (CVAE) as the speech synthesiser. Meanwhile, an adaptive sign gradient descent algorithm is proposed to solve the adversarial audio synthesis task. Experiments on three datasets (i.e., Audio Mnist, Common Voice, and Librispeech) show that our method could synthesise audio adversarial examples that are naturally sounded but misleading the start-of-the-art ASR models. The project webpage containing generated audio demos is at https://sites.google.com/view/ssa-asr/home.
Transductive Universal Transport for Zero-Shot Action Recognition
Authors: Pascal Mettes
Date: 2021-09-29 00:00:00
Tasks: Action Recognition, Object, Position, Temporal Localization, Zero-Shot Action Recognition, zero-shot-classification, Zero-Shot Learning

This work addresses the problem of recognizing action categories in videos for which no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a shared semantic space, either trained on large-scale seen actions or on objects. While effective, universal action and object models are biased to their seen categories. Such biases are further amplified due to biases between seen and unseen categories in the semantic space. The amplified biases result in many unseen action categories simply never being selected during inference, hampering zero-shot progress. We seeks to address this limitation and introduce transductive universal transport for zero-shot action recognition. Our proposal is to re-position unseen action embeddings through transduction, \ie by using the distribution of the unlabelled test set. For universal action models, we first find an optimal mapping from unseen actions to the mapped test videos in the shared hyperspherical space. We then define target embeddings as weighted Fr\'echet means, with the weights given by the transport couplings. Finally, we re-position unseen action embeddings along the geodesic between the original and target, as a form of semantic regularization. For universal object models, we outline a weighted transport variant from unseen action embeddings to object embeddings directly. Empirically, we show that our approach directly boosts universal action and object models, resulting in state-of-the-art performance for zero-shot classification and spatio-temporal localization.
Cyclic Test Time Augmentation with Entropy Weight Method
Authors: Sewhan Chun, Jae Young Lee, Junmo Kim
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation

In the recent studies of data augmentation of neural networks, the application of test time augmentation has been studied to extract optimal transformation policies to enhance performance with minimum cost. The policy search method with the best level of input data dependency involves training a loss predictor network to estimate suitable transformations for each of the given input image in independent manner, resulting in instance-level transformation extraction. In this work, we propose a method to utilize and modify the loss prediction pipeline to further improve the performance with the cyclic search for suitable transformations and the use of the entropy weight method. The cyclic usage of the loss predictor allows refining each input image with multiple transformations with a more flexible transformation magnitude. For cases where multiple augmentations are generated, we implement the entropy weight method to reflect the data uncertainty of each augmentation to force the final result to focus on augmentations with low uncertainty. The experimental result shows convincing qualitative outcome and robust performance for the corrupted conditions of data.
Disentangled Representations using Trained Models
Authors: Eva Smit, Thomas Gärtner, Patrick Forré
Date: 2021-09-29 00:00:00

We propose a novel method to learn disentangled representations. The ability to compute a disentangled representation is useful for many tasks because it contains information about samples from a dataset in an interpretable and compact structure. Thus development of a method that learns disentangled representations is an active area of research. In contrast to previously proposed methods, we neither require access to the values of the interpretable factors, nor to information about groups of data samples which share the values of some interpretable factors. Our proposed algorithm uses only a set of models which already have been trained on the data. With the help of the implicit function theorem we show how, using a diverse set of models that have already been trained on the data, to select a pair of data points that have a common value of interpretable factors. We prove that such an auxiliary sampler is sufficient to obtain a disentangled representation. Based on this theoretical result, we propose a loss function that the method should optimize to compute the disentangled representation. Our approach is easy to implement and shows promising results in simulations.
Subpixel object segmentation using wavelets and multiresolution analysis
Authors: Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Date: 2021-09-29 00:00:00
Tasks: Decoder, Object, Semantic Segmentation

We propose a novel deep learning framework for fast prediction of boundaries of two-dimensional simply connected domains using wavelets and Multiresolution Analysis (MRA). The boundaries are modelled as (piecewise) smooth closed curves using wavelets and the so-called Pyramid Algorithm. Our network architecture is a hybrid analog of the U-Net, where the down-sampling path is a two-dimensional encoder with learnable filters, and the upsampling path is a one-dimensional decoder, which builds curves up from low to high resolution levels. Any wavelet basis induced by a MRA can be used. This flexibility allows for incorporation of priors on the smoothness of curves. The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline. Our model demonstrates up to 5x faster inference speed compared to the U-Net, while maintaining similar performance in terms of Dice score and Hausdorff distance.
Less is more: Selecting the right benchmarking set of data for time series classification
Authors: Tome Eftimov, Gašper Petelin, Gjorgjina Cenikj, Ana Kostovska, Gordana Ispirova, Peter Korošec, Jasmin Bogatinovski
Date: 2021-09-29 00:00:00
Tasks: Benchmarking, Time Series, Time Series Analysis, Time Series Classification

In this paper, we have proposed a new pipeline for landscape analysis of machine learning datasets that enables us to better understand a benchmarking problem landscape, allows us to select a diverse benchmark datasets portfolio, and identify the presence of performance assessment bias via bootstrapping evaluation. Combining a large multi-domain representation corpus of time-series specific features and the results of a large empirical study of time-series classification benchmark, we showcase the capability of the pipeline to point out issues with non-redundancy and representativeness in the benchmark. By observing discrepancy between the empirical results of the bootstrap evaluation and recently adapted practices in TSC literature when introducing novel methods we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study). Finally, we propose a set of datasets uniformly distributed across the landscape space one should consider when benchmarking novel TSC methods.
Neural Photometric Stereo for Shape and Material Estimation
Authors: Junxuan Li, Hongdong Li
Date: 2021-09-29 00:00:00

This paper addresses a challenging Photometric-Stereo problem where the object to be reconstructed has unknown, non-Lambertian, and possibly spatially-varying surface materials. This problem becomes even more challenging when the shape of the object is highly complex so that shadows cast on the surface are inevitable.  To overcome these challenges, we propose a simple coordinate-based deep MLP (multilayer perceptron) neural network to parameterize both the unknown 3D shape and the unknown spatially-varying reflectance at every image pixel. This network is able to leverage the observed specularities and shadows on the surface, and recover both surface shape, normal and generic non-Lambertian reflectance via an inverse differentiable rendering process.  We explicitly predict cast shadows, mitigating possible artifacts on these shadowing regions, leading to higher estimation accuracy. Our framework is entirely self-supervised, in the sense that it requires neither ground truth shape nor known svBRDF. Tests on real-world images demonstrate that our method achieves state-of-the-art accuracy in both shape recovery and material estimation. Thanks to the small size of the MLP-net, our method is also an order of magnitude faster than previous competing deep-learning based photometric stereo methods.
Towards Predictable Feature Attribution: Revisiting and Improving Guided BackPropagation
Authors: Guanhua Zheng, Jitao Sang, Wang Haonan, Changsheng Xu
Date: 2021-09-29 00:00:00
Tasks: Decision Making

Recently, backpropagation(BP)-based feature attribution methods have been widely adopted to interpret the internal mechanisms of convolutional neural networks (CNNs), and expected to be human-understandable (lucidity) and faithful to decision-making processes (fidelity). In this paper, we introduce a novel property for feature attribution: predictability, which means users can forecast behaviors of the interpretation methods. With the evidence that many attribution methods have unexpected and harmful phenomena like class-insensitivity, the predictability is critical to avoid over-trust and misuse from users. Observing that many intuitive improvements for lucidity and fidelity tend to sacrifice predictability, we propose a new visual explanation method called TR-GBP (Theoretical Refinements of Guided BackPropagation) which revisits and improves GBP from theoretical perspective rather than solely optimizing the attribution performance. Qualitative and quantitative experiments show that TR-GBP is more visually sharpened, gets rid of the fidelity problems in GBP, and effectively predicts the possible behaviors so that we can easily discriminate some prediction errors from interpretation errors. The codes of TR-GBP are available in supplementary and will be open source.
Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation
Authors: Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Pseudo Label, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.
Source-free UDA, which is a more challenging UDA task, can access the pre-trained model within the source domain.
The pre-trained model, however, provides a noisy pseudo-label; thus, source-free UDA requires robust training.
In this study, we propose a Confidence score Weighting Adaptation (CoWA), which is a simple yet effective source-free UDA method. 
CoWA utilizes the Joint Model-Data Structure (JMDS) confidence score designed for source-free UDA as a sample-wise weight.
As components of CoWA, we introduce Suppressed Cross Entropy (SCE) loss and a weight mixup to robustly leverage the low-confidence samples.
Experiment results show that CoWA achieves a superior performance compared to other source-free UDA methods on various UDA benchmarks including open-set and partial-set domain adaptation.
Furthermore, on several benchmarks, CoWA surpasses state-of-the-art conventional UDA methods that use labeled source domain data.
Variational Inference via Resolution of Singularities
Authors: Susan Wei
Date: 2021-09-29 00:00:00
Tasks: Learning Theory, Variational Inference

Predicated on the premise that neural networks are best viewed as singular statistical models, we set out to propose a new variational approximation for Bayesian neural networks. The approximation relies on a central result from singular learning theory according to which the posterior distribution over the parameters of a singular model, following an algebraic-geometrical transformation known as a desingularization map, is asymptotically a mixture of standard forms. From here we proceed to demonstrate that a generalized gamma mean-field variational family, following desingularization, can recover the leading order term of the model evidence. Affine coupling layers are employed to learn the unknown desingularization map, effectively rendering the proposed methodology a normalizing flow with the generalized gamma as the source distribution.