pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Convolutional Neural Networks
Authors: Julio Cesar Zamora, Jesus Adan Cruz Vargas, Omesh Tickoo
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

The quest for increased visual recognition performance has led to the development of highly complex neural networks with very deep topologies. To avoid high computing resource requirements of such complex networks and to enable operation on devices with limited resources, this paper introduces adaptive kernels for convolutional layers. Motivated by the non-linear perception response in human visual cells, the input image is used to define the weights of a dynamic kernel called Adaptive kernel. This new adaptive kernel is used to perform a second convolution of the input image generating the output pixel. Adaptive kernels enable accurate recognition with lower memory requirements; This is accomplished through reducing the number of kernels and the number of layers needed in the typical CNN configuration, in addition to reducing the memory used, increasing 2X the training speed and the number of activation function evaluations. Our experiments show a reduction of 70X in the memory used for MNIST, maintaining 99% accuracy and 16X memory reduction for CIFAR10 with 92.5% accuracy.
Explicit Recall for Efficient Exploration
Authors: Honghua Dong, Jiayuan Mao, Xinyue Cui, Lihong Li
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making, Deep Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning.  This memory records structured trajectories that have led to interesting states in the past, and can be used by the agent to revisit those states more effectively.  In high-dimensional decision making problems, where deep reinforcement learning is considered crucial, our approach provides a simple, transparent and effective way that can be naturally combined with complex, deep learning models.  We show how such explicit memory may be used to enhance existing exploration algorithms such as intrinsically motivated ones and count-based ones, and demonstrate our method's advantages in various simulated environments.
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
Authors: Shuhui Qu, Janghwan Lee, Wei Xiong, Wonhyouk Jang, Jie Wang
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Anomaly Detection, Generative Adversarial Network, One-Class Classification

One class anomaly detection on high-dimensional data is one of the critical issue in both fundamental machine learning research area and manufacturing applica- tions. A good anomaly detection should accurately discriminate anomalies from normal data. Although most previous anomaly detection methods achieve good performances, they do not perform well on high-dimensional imbalanced data- set 1) with a limited amount of data; 2) multi-modal distribution; 3) few anomaly data. In this paper, we develop a multi-modal one-class generative adversarial net- work based detector (MMOC-GAN) to distinguish anomalies from normal data (products). Apart from a domain-specific feature extractor, our model leverage a generative adversarial network(GAN). The generator takes in a modified noise vector using a pseudo latent prior and generate samples at the low-density area of the given normal data to simulate the anomalies. The discriminator then is trained to distinguish the generate samples from the normal samples. Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data. Experiments demonstrate that our model outperforms the state-of-the-art one-class classification models and other anomaly detection methods on both normal data and anomalies accuracy, as well as the F1 score. Also, the generated samples can fully capture the low density area of different types of products.
Learning to encode spatial relations from natural language
Authors: Tiago Ramalho, Tomas Kocisky‎, Frederic Besse, S. M. Ali Eslami, Gabor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Spatial Reasoning

Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world. In particular, spatial relations are encoded in a way that is inconsistent with human spatial reasoning and lacking invariance to viewpoint changes. We present a system capable of capturing the semantics of spatial relations such as behind, left of, etc from natural language. Our key contributions are a novel multi-modal objective based on generating images of scenes from their textual descriptions, and a new dataset on which to train it. We demonstrate that internal representations are robust to meaning preserving transformations of descriptions (paraphrase invariance), while viewpoint invariance is an emergent property of the system.
D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
Authors: Florent CHIARONI. Mohamed-Cherif RAHAL. Nicolas HUEBER. Frédéric DUFAUX.
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network

Positive Unlabeled (PU) learning consists in learning to distinguish samples of our class of interest, the positive class, from the counter-examples, the negative class, by using positive labeled and unlabeled samples during the training. Recent approaches exploit the GANs abilities to address the PU learning problem by generating relevant counter-examples. In this paper, we propose a new GAN-based PU learning approach named Divergent-GAN (D-GAN). The key idea is to incorporate a standard Positive Unlabeled learning risk inside the GAN discriminator loss function. In this way, the discriminator can ask the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution. This enables the generator convergence towards the unlabeled counter-examples distribution without using prior knowledge, while keeping the standard adversarial GAN architecture. In addition, we discuss normalization techniques in the context of the proposed framework. Experimental results show that the proposed approach overcomes previous GAN-based PU learning methods issues, and it globally outperforms two-stage state of the art PU learning performances in terms of stability and prediction on both simple and complex image datasets.
Where and when to look? Spatial-temporal attention for action recognition in videos
Authors: Lili Meng, Bo Zhao, Bo Chang, Gao Huang, Frederick Tung, Leonid Sigal
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization, Video Classification

Inspired by the observation that humans are able to process videos efficiently by only paying attention when and where it is needed, we propose a novel spatial-temporal attention mechanism for video-based action recognition. For spatial attention, we learn a saliency mask to allow the model to focus on the most salient parts of the feature maps. 
For temporal attention, we employ a soft temporal attention mechanism to identify the most relevant frames from an input video. Further, we propose a set of regularizers that ensure that our attention mechanism attends to coherent regions in space and time. Our model is efficient, as it proposes a separable spatio-temporal mechanism for video attention, while being able to identify important parts of the video both spatially and temporally.  We demonstrate the efficacy of our approach on three public video action recognition datasets. The proposed approach leads to state-of-the-art performance on all of them, including the new large-scale Moments in Time dataset. Furthermore, we quantitatively and qualitatively evaluate our model's ability to accurately localize discriminative regions spatially and critical frames temporally. This is despite our model only being trained with per video classification labels.
Integrated Steganography and Steganalysis with Generative Adversarial Networks
Authors: Chong Yu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network, Steganalysis

Recently, generative adversarial network is the hotspot in research areas and industrial application areas. It's application on data generation in computer vision is most common usage. This paper extends its application to data hiding and security area. In this paper, we propose the novel framework to integrate steganography and steganalysis processes. The proposed framework applies generative adversarial networks as the core structure. The discriminative model simulate the steganalysis process, which can help us understand the sensitivity of cover images to semantic changes. The steganography generative model is to generate stego image which is aligned with the original cover image, and attempts to confuse steganalysis discriminative model. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality and security of generated stego image in the iterative training process. Training dataset is mixed with intact images as well as intentional attacked images. The mix training process can further improve the robustness and security of new framework. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in steganography and steganalysis benchmarks.
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
Authors: Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making

We present a soft, spatial, sequential, top-down attention model (S3TA). This model uses a soft attention mechanism to bottleneck its view of the input. A recurrent core is used to generate query vectors, which actively select information from the input by correlating the query with input- and space-dependent key maps at different spatial locations.

We demonstrate the power and interpretabilty of this model under two settings. First, we build an agent which uses this attention model in RL environments and show that we can achieve performance competitive with state-of-the-art models while producing attention maps that elucidate some of the strategies used to solve the task. Second, we use this model in supervised learning tasks and show that it also achieves competitive performance and provides interpretable attention maps that show some of the underlying logic in the model's decision making.
ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
Authors: Tin Lay Nwe, Shudong Xie, Balaji Nataraj, Yiqun Li, Joo-Hwee Lim
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Incremental Learning

Context Aware Advertisement (CAA) is a type of advertisement
appearing on websites or mobile apps. The advertisement is targeted
on specific group of users and/or the content displayed on the
websites or apps. This paper focuses on classifying images displayed
on the websites by incremental learning classifier with Deep
Convolutional Neural Network (DCNN) especially for Context Aware
Advertisement (CAA) framework. Incrementally learning new knowledge
with DCNN leads to catastrophic forgetting as previously stored
information is replaced with new information. To prevent
catastrophic forgetting, part of previously learned knowledge should
be stored for the life time of incremental classifier. Storing
information for life time involves privacy and legal concerns
especially in context aware advertising framework. Here, we propose
an incremental classifier learning method which addresses privacy
and legal concerns while taking care of catastrophic forgetting
problem. We conduct experiments on different datasets including
CIFAR-100. Experimental results show that proposed system achieves
relatively high performance compared to the state-of-the-art
incremental learning methods.
Look Ma, No GANs! Image Transformation with ModifAE
Authors: Chad Atalla, Bartholomew Tam, Amanda Song, Gary Cottrell
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-to-Image Translation, Translation

Existing methods of image to image translation require multiple steps in the training or modification process, and suffer from either an inability to generalize, or long training times. These methods also focus on binary trait modification, ignoring continuous traits. To address these problems, we propose ModifAE: a novel standalone neural network, trained exclusively on an autoencoding task, that implicitly learns to make continuous trait image modifications. As a standalone image modification network, ModifAE requires fewer parameters and less time to train than existing models. We empirically show that ModifAE produces significantly more convincing and more consistent continuous face trait modifications than the previous state-of-the-art model.
Object detection deep learning networks for Optical Character Recognition
Authors: Christopher Bourez, Aurelien Coquard
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Learning, Document Classification, General Classification, Object, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR)

In this article, we show how we applied a simple approach coming from deep learning networks for object detection to the task of optical character recognition in order to build image features taylored for documents. In contrast to scene text reading in natural images using networks pretrained on ImageNet, our document reading is performed with small networks inspired by MNIST digit recognition challenge, at a small computational budget and a small stride. The object detection modern frameworks allow a direct end-to-end training, with no other algorithm than the deep learning and the non-max-suppression algorithm to filter the duplicate predictions. The trained weights can be used for higher level models, such as, for example, document classification, or document segmentation.
Learning what and where to attend with humans in the loop
Authors: Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diagnostic, Image Categorization, Object Recognition

Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.
Learning to Drive by Observing the Best and Synthesizing the Worst
Authors: Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Autonomous Driving, Imitation Learning

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world ( https://sites.google.com/view/learn-to-drive ).
An adaptive homeostatic algorithm for the unsupervised learning of visual features
Authors: Victor Boutin, Angelo Franciosini, Laurent Perrinet
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

The formation of structure in the brain, that is, of the connections between cells within neural populations, is by large an unsupervised learning process: the emergence of this architecture is mostly self-organized. In the primary visual cortex of mammals, for example, one may observe during development the formation of cells selective to localized, oriented features. This leads to the development of a rough representation of contours of the retinal image in area V1. We modeled these mechanisms using sparse Hebbian learning algorithms. These algorithms alternate a coding step to encode the information with a learning step to find the proper encoder. A major difficulty faced by these algorithms is to deduce a good representation while knowing immature encoders, and to learn good encoders with a non-optimal representation. To address this problem, we propose to introduce a new regulation process between learning and coding, called homeostasis. Our homeostasis is compatible with a neuro-mimetic architecture and allows for the fast emergence of localized filters sensitive to orientation. The key to this algorithm lies in a simple adaptation mechanism based on non-linear functions that reconciles the antagonistic processes that occur at the coding and learning time scales. We tested this unsupervised algorithm with this homeostasis rule for a range of existing unsupervised learning algorithms coupled with different neural coding algorithms. In addition, we propose a simplification of this optimal homeostasis rule by implementing a simple heuristic on the probability of activation of neurons. Compared to the optimal homeostasis rule, we show that this heuristic allows to implement a more rapid unsupervised learning algorithm while keeping a large part of its effectiveness. These results demonstrate the potential application of such a strategy in machine learning and we illustrate this with one result in a convolutional neural network.
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks
Authors: Saeid Asgari Taghanaki, Shekoofeh Azizi, Ghassan Hamarneh
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: image-classification, Image Classification

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, in this paper, we propose a nonlinear radial basis convolutional feature transformation by learning the Mahalanobis distance function that maps the input convolutional features from the same class into tight clusters. In such a space, the clusters become compact and well-separated, which prevent small adversarial perturbations from forcing a sample to cross the decision boundary. We test the proposed method on three publicly available image classification and segmentation data-sets namely, MNIST, ISBI ISIC skin lesion, and NIH ChestX-ray14. We evaluate the robustness of our method to different gradient (targeted and untargeted) and non-gradient based attacks and compare it to several non-gradient masking defense strategies. Our results demonstrate that the proposed method can boost the performance of deep convolutional neural networks against adversarial perturbations without accuracy drop on clean data.
Machine Translation With Weakly Paired Bilingual Documents
Authors: Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Xu Tan, Tao Qin, Tie-Yan Liu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, Sentence, Translation, Unsupervised Machine Translation

Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the availability of large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual data only, leading to much lower accuracy compared with the supervised one. Observing that weakly paired bilingual documents are much easier to collect than bilingual sentences, e.g., from Wikipedia, news websites or books, in this paper, we investigate the training of translation models with weakly paired bilingual documents. Our approach contains two components/steps. First, we provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised signals for training. Second, we leverage the topic consistency of two weakly paired documents and learn the sentence-to-sentence translation by constraining the word distribution-level alignments.  We evaluate our proposed method on weakly paired documents from Wikipedia on four tasks, the widely used WMT16 German$\leftrightarrow$English and WMT13 Spanish$\leftrightarrow$English tasks, and obtain $24.1$/$30.3$ and $28.0$/$27.6$ BLEU points separately, outperforming
state-of-the-art unsupervised results by more than 5 BLEU points and reducing the gap between unsupervised translation and supervised translation up to 50\%.
Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning
Authors: Hariharan Ravishankar, Rahul Venkataramani, Saihareesh Anamandra, Prasad Sudhakar
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Lifelong learning, Representation Learning

Despite the recent advances in representation learning, lifelong learning continues
to be one of the most challenging and unconquered problems. Catastrophic forgetting
and data privacy constitute two of the important challenges for a successful
lifelong learner. Further, existing techniques are designed to handle only specific
manifestations of lifelong learning, whereas a practical lifelong learner is expected
to switch and adapt seamlessly to different scenarios. In this paper, we present a
single, unified mathematical framework for handling the myriad variants of lifelong
learning, while alleviating these two challenges. We utilize an external memory
to store only the features representing past data and learn richer and newer
representations incrementally through transformation neural networks - feature
transformers. We define, simulate and demonstrate exemplary performance on a
realistic lifelong experimental setting using the MNIST rotations dataset, paving
the way for practical lifelong learners. To illustrate the applicability of our method
in data sensitive domains like healthcare, we study the pneumothorax classification
problem from X-ray images, achieving near gold standard performance.
We also benchmark our approach with a number of state-of-the art methods on
MNIST rotations and iCIFAR100 datasets demonstrating superior performance.