pwc-archive/papers-with-abstracts huggingface dataset:
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Authors: Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
Date: 2021-02-16 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Image Classification, Language Modeling, Language Modelling, Machine Translation, Translation

Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision. Unfortunately, novel architectures often result in challenging hyper-parameter choices and training instability if the network parameters are not properly initialized. A number of architecture-specific initialization schemes have been proposed, but these schemes are not always portable to new architectures. This paper presents GradInit, an automated and architecture agnostic method for initializing neural networks. GradInit is based on a simple heuristic; the norm of each network layer is adjusted so that a single step of SGD or Adam with prescribed hyperparameters results in the smallest possible loss value. This adjustment is done by introducing a scalar multiplier variable in front of each parameter block, and then optimizing these variables using a simple numerical scheme. GradInit accelerates the convergence and test performance of many convolutional architectures, both with or without skip connections, and even without normalization layers. It also improves the stability of the original Transformer architecture for machine translation, enabling training it without learning rate warmup using either Adam or SGD under a wide range of learning rates and momentum coefficients. Code is available at https://github.com/zhuchen03/gradinit.
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Authors: Andras Kupcsik, Markus Spies, Alexander Klein, Marco Todescato, Nicolai Waniek, Philipp Schillinger, Mathias Buerger
Date: 2021-02-16 00:00:00
Tasks: Object

Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object into a descriptor space image, which implicitly encodes key features of an object invariant to the relative camera pose. Impressively, the self-supervised training of DONs can be applied to arbitrary objects and can be evaluated and deployed within hours. However, the training approach relies on accurate depth images and faces challenges with small, reflective objects, typical for industrial settings, when using consumer grade depth cameras. In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. We rely on Laplacian Eigenmaps (LE) to embed the 3D model of an object into an optimally generated space. While our approach uses more domain knowledge, it can be efficiently applied even for smaller and reflective objects, as it does not rely on depth information. We compare the training methods on generating 6D grasps for industrial objects and show that our novel supervised training approach improves the pick-and-place performance in industry-relevant tasks.
Composing Pick-and-Place Tasks By Grounding Language
Authors: Oier Mees, Wolfram Burgard
Date: 2021-02-16 00:00:00
Tasks: Natural Language Visual Grounding, Robotic Grasping, Visual Grounding

Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues. Our approach infers objects and their relationships from input images and language expressions and can place objects in accordance with the spatial relations expressed by the user. Unlike previous approaches, we consider grounding not only for the picking but also for the placement of everyday objects from language. Specifically, by grounding objects and their spatial relations, we allow specification of complex placement instructions, e.g. "place it behind the middle red bowl". Our results obtained using a real-world PR2 robot demonstrate the effectiveness of our method in understanding pick-and-place language instructions and sequentially composing them to solve tabletop manipulation tasks. Videos are available at http://speechrobot.cs.uni-freiburg.de
A Law of Robustness for Weight-bounded Neural Networks
Authors: Hisham Husain, Borja Balle
Date: 2021-02-16 00:00:00

Robustness of deep neural networks against adversarial perturbations is a pressing concern motivated by recent findings showing the pervasive nature of such vulnerabilities. One method of characterizing the robustness of a neural network model is through its Lipschitz constant, which forms a robustness certificate. A natural question to ask is, for a fixed model class (such as neural networks) and a dataset of size $n$, what is the smallest achievable Lipschitz constant among all models that fit the dataset? Recently, (Bubeck et al., 2020) conjectured that when using two-layer networks with $k$ neurons to fit a generic dataset, the smallest Lipschitz constant is $\Omega(\sqrt{\frac{n}{k}})$. This implies that one would require one neuron per data point to robustly fit the data. In this work we derive a lower bound on the Lipschitz constant for any arbitrary model class with bounded Rademacher complexity. Our result coincides with that conjectured in (Bubeck et al., 2020) for two-layer networks under the assumption of bounded weights. However, due to our result's generality, we also derive bounds for multi-layer neural networks, discovering that one requires $\log n$ constant-sized layers to robustly fit the data. Thus, our work establishes a law of robustness for weight bounded neural networks and provides formal evidence on the necessity of over-parametrization in deep learning.
An AutoML-based Approach to Multimodal Image Sentiment Analysis
Authors: Vasco Lopes, António Gaspar, Luís A. Alexandre, João Cordeiro
Date: 2021-02-16 00:00:00
Tasks: AutoML, Marketing, Multimodal Sentiment Analysis, Recommendation Systems, Sentiment Analysis

Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy.
Making the most of your day: online learning for optimal allocation of time
Authors: Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini
Date: 2021-02-16 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Scheduling

We study online learning for optimal allocation when the resource to be allocated is time. %Examples of possible applications include job scheduling for a computing server, a driver filling a day with rides, a landlord renting an estate, etc. An agent receives task proposals sequentially according to a Poisson process and can either accept or reject a proposed task. If she accepts the proposal, she is busy for the duration of the task and obtains a reward that depends on the task duration. If she rejects it, she remains on hold until a new task proposal arrives. We study the regret incurred by the agent, first when she knows her reward function but does not know the distribution of the task duration, and then when she does not know her reward function, either. This natural setting bears similarities with contextual (one-armed) bandits, but with the crucial difference that the normalized reward associated to a context depends on the whole distribution of contexts.
Boosting Deep Transfer Learning for COVID-19 Classification
Authors: Fouzia Altaf, Syed M. S. Islam, Naeem K. Janjua, Naveed Akhtar
Date: 2021-02-16 00:00:00
Tasks: Classification, Computed Tomography (CT), Data Augmentation, General Classification, Representation Learning, Transfer Learning

COVID-19 classification using chest Computed Tomography (CT) has been found pragmatically useful by several studies. Due to the lack of annotated samples, these studies recommend transfer learning and explore the choices of pre-trained models and data augmentation. However, it is still unknown if there are better strategies than vanilla transfer learning for more accurate COVID-19 classification with limited CT data. This paper provides an affirmative answer, devising a novel `model' augmentation technique that allows a considerable performance boost to transfer learning for the task. Our method systematically reduces the distributional shift between the source and target domains and considers augmenting deep learning with complementary representation learning techniques. We establish the efficacy of our method with publicly available datasets and models, along with identifying contrasting observations in the previous studies.
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
Authors: Subham Nagar, Ahlad Kumar, M. N. S. Swamy
Date: 2021-02-16 00:00:00
Tasks: Denoising, EEG, Electroencephalogram (EEG)

A fractional-based compressed auto-encoder architecture has been introduced to solve the problem of denoising electroencephalogram (EEG) signals. The architecture makes use of fractional calculus to calculate the gradients during the backpropagation process, as a result of which a new hyper-parameter in the form of fractional order ($\alpha$) has been introduced which can be tuned to get the best denoising performance. Additionally, to avoid substantial use of memory resources, the model makes use of orthogonal features in the form of Tchebichef moments as input. The orthogonal features have been used in achieving compression at the input stage. Considering the growing use of low energy devices, compression of neural networks becomes imperative. Here, the auto-encoder's weights are compressed using the randomized singular value decomposition (RSVD) algorithm during training while evaluation is performed using various compression ratios. The experimental results show that the proposed fractionally compressed architecture provides improved denoising results on the standard datasets when compared with the existing methods.
Learning the Noise of Failure: Intelligent System Tests for Robots
Authors: Felix Sygulla, Daniel Rixen
Date: 2021-02-16 00:00:00

Roboticists usually test new control software in simulation environments before evaluating its functionality on real-world robots. Simulations reduce the risk of damaging the hardware and can significantly increase the development process's efficiency in the form of automated system tests. However, many flaws in the software remain undetected in simulation data, revealing their harmful effects on the system only in time-consuming experiments. In reality, such irregularities are often easily recognized solely by the robot's airborne noise during operation. We propose a simulated noise estimate for the detection of failures in automated system tests of robots. The classification of flaws uses classical machine learning - a support vector machine - to identify different failure classes from the scalar noise estimate. The methodology is evaluated on simulation data from the humanoid robot LOLA. The approach yields high failure detection accuracy with a low false-positive rate, enabling its use for stricter automated system tests. Results indicate that a single trained model may work for different robots. The proposed technique is provided to the community in the form of the open-source tool NoisyTest, making it easy to test data from any robot. In a broader scope, the technique may empower real-world automated system tests without human evaluation of success or failure.
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation
Authors: Adil Kaan Akan, Emre Akbas, Fatos T. Yarman Vural
Date: 2021-02-16 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Image Generation, object-detection, Object Detection

In this study, we introduce a measure for machine perception, inspired by the concept of Just Noticeable Difference (JND) of human perception. Based on this measure, we suggest an adversarial image generation algorithm, which iteratively distorts an image by an additive noise until the model detects the change in the image by outputting a false label. The noise added to the original image is defined as the gradient of the cost function of the model. A novel cost function is defined to explicitly minimize the amount of perturbation applied to the input image while enforcing the perceptual similarity between the adversarial and input images. For this purpose, the cost function is regularized by the well-known total variation and bounded range terms to meet the natural appearance of the adversarial image. We evaluate the adversarial images generated by our algorithm both qualitatively and quantitatively on CIFAR10, ImageNet, and MS COCO datasets. Our experiments on image classification and object detection tasks show that adversarial images generated by our JND method are both more successful in deceiving the recognition/detection models and less perturbed compared to the images generated by the state-of-the-art methods, namely, FGV, FSGM, and DeepFool methods.
Restore from Restored: Single-image Inpainting
Authors: Eunhye Lee, Jeongmu Kim, Jisu Kim, Tae Hyun Kim
Date: 2021-02-16 00:00:00
Tasks: Image Inpainting

Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks are still limited in exploiting internal information available in the given input image at test time. To mitigate this problem, we present a novel and efficient self-supervised fine-tuning algorithm that can adapt the parameters of fully pre-trained inpainting networks without using ground-truth target images. We update the parameters of the pre-trained state-of-the-art inpainting networks by utilizing existing self-similar patches within the given input image without changing network architecture and improve the inpainting quality by a large margin. Qualitative and quantitative experimental results demonstrate the superiority of the proposed algorithm, and we achieve state-of-the-art inpainting results on publicly available numerous benchmark datasets.
Axial Residual Networks for CycleGAN-based Voice Conversion
Authors: Jaeseong You, Gyuhyeon Nam, Dalhyun Kim, Gyeongsu Chae
Date: 2021-02-16 00:00:00
Tasks: Voice Conversion

We propose a novel architecture and improved training objectives for non-parallel voice conversion. Our proposed CycleGAN-based model performs a shape-preserving transformation directly on a high frequency-resolution magnitude spectrogram, converting its style (i.e. speaker identity) while preserving the speech content. Throughout the entire conversion process, the model does not resort to compressed intermediate representations of any sort (e.g. mel spectrogram, low resolution spectrogram, decomposed network feature). We propose an efficient axial residual block architecture to support this expensive procedure and various modifications to the CycleGAN losses to stabilize the training process. We demonstrate via experiments that our proposed model outperforms Scyclone and shows a comparable or better performance to that of CycleGAN-VC2 even without employing a neural vocoder.
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
Authors: Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2021-02-16 00:00:00
Tasks: Audio Classification, Event Detection, Few-Shot Audio Classification, Few-Shot Learning, General Classification, Meta-Learning, Metric Learning, Speaker Recognition, Triplet

Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the form of a mean vector of the embedded support points within a class. The performance of prototypical networks in extreme few-shot scenarios (like one-shot) degrades drastically, mainly due to the desuetude of variations within the clusters while constructing prototypes. In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique. The conventional triplet selection leads to overfitting, because of all possible combinations being used during training. We incorporate episodic training for mining the semi hard positive and the semi hard negative triplets to overcome the overfitting. We also propose an adaptation to make use of unlabeled training samples for better modeling. Experimenting on two different audio processing tasks, namely speaker recognition and audio event detection; show improved performances and hence the efficacy of ETM over the prototypical loss function and other meta-learning frameworks. Further, we show improved performances when unlabeled training samples are used.
Steadily Learn to Drive with Virtual Memory
Authors: Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, Jianyu Chen
Date: 2021-02-16 00:00:00
Tasks: Autonomous Driving

Reinforcement learning has shown great potential in developing high-level autonomous driving. However, for high-dimensional tasks, current RL methods suffer from low data efficiency and oscillation in the training process. This paper proposes an algorithm called Learn to drive with Virtual Memory (LVM) to overcome these problems. LVM compresses the high-dimensional information into compact latent states and learns a latent dynamic model to summarize the agent's experience. Various imagined latent trajectories are generated as virtual memory by the latent dynamic model. The policy is learned by propagating gradient through the learned latent model with the imagined latent trajectories and thus leads to high data efficiency. Furthermore, a double critic structure is designed to reduce the oscillation during the training process. The effectiveness of LVM is demonstrated by an image-input autonomous driving task, in which LVM outperforms the existing method in terms of data efficiency, learning stability, and control performance.
A comparative study on movement feature in different directions for micro-expression recognition
Authors: Jinsheng Wei, Guanming Lu, Jingjie Yan
Date: 2021-02-16 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition

Micro-expression can reflect people's real emotions. Recognizing micro-expressions is difficult because they are small motions and have a short duration. As the research is deepening into micro-expression recognition, many effective features and methods have been proposed. To determine which direction of movement feature is easier for distinguishing micro-expressions, this paper selects 18 directions (including three types of horizontal, vertical and oblique movements) and proposes a new low-dimensional feature called the Histogram of Single Direction Gradient (HSDG) to study this topic. In this paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP with Single Direction Gradient (LBP-SDG) and analyze which direction of movement feature is more discriminative for micro-expression recognition. As with some existing work, Euler Video Magnification (EVM) is employed as a preprocessing step. The experiments on the CASME II and SMIC-HS databases summarize the effective and optimal directions and demonstrate that HSDG in an optimal direction is discriminative, and the corresponding LBP-SDG achieves state-of-the-art performance using EVM.
Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries
Authors: Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
Date: 2021-02-16 00:00:00
Tasks: Action Recognition, Object, Temporal Action Localization

We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance.
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
Authors: Ozan Ozdenizci, Deniz Erdogmus
Date: 2021-02-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery, Time Series, Time Series Analysis

Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on generating artificial electroencephalography (EEG) signals with deep neural networks. Subsequently, we present our feasibility experiments on generating condition-specific multichannel EEG signals using conditional variational autoencoders. By manipulating real resting-state EEG epochs, we present an approach to synthetically generate time-series multichannel signals that show spectro-temporal EEG patterns which are expected to be observed during distinct motor imagery conditions.