pwc-archive/papers-with-abstracts huggingface dataset:
A Seed-Augment-Train Framework for Universal Digit Classification
Authors: Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Classification, Dataset Generation, Transfer Learning

In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also qualitatively by testing a CNN trained on the synthetic data on the real-world datasets. This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.
Variational autoencoders trained with q-deformed lower bounds
Authors: Septimia Sârbu, Luigi Malagò
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Variational Inference

Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies. At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data. In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence. The latent representation and the performance of VAEs are heavily influenced by the type of bound used as a cost function. Significant research work has been carried out into the development of tighter bounds than the original ELBO, to more accurately approximate the true log-likelihood. By leveraging the q-deformed logarithm in the traditional lower bounds, ELBO and IWAE, and the upper bound CUBO, we bring contributions to this direction of research. In this proof-of-concept study, we explore different ways of creating these q-deformed bounds that are tighter than the classical ones and we show improvements in the performance of such VAEs on the binarized MNIST dataset.
Improved Adversarial Image Captioning
Authors: Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Tom Sercu
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Image Captioning

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.
DUAL SPACE LEARNING WITH VARIATIONAL AUTOENCODERS
Authors: Hirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Missing Values

This paper proposes a dual variational autoencoder (DualVAE), a framework for generating images corresponding to multiclass labels. Recent research on conditional generative models, such as the Conditional VAE, exhibit image transfer by changing labels. However, when the dimension of multiclass labels is large, these models cannot change images corresponding to labels, because learning multiple distributions of the corresponding class is necessary to transfer an image. This leads to the lack of training data. Therefore, instead of conditioning with labels, we condition with latent vectors that include label information. DualVAE divides one distribution of the latent space by linear decision boundaries using labels. Consequently, DualVAE can easily transfer an image by moving a latent vector toward a decision boundary and is robust to the missing values of multiclass labels. To evaluate our proposed method, we introduce a conditional inception score (CIS) for measuring how much an image changes to the target class. We evaluate the images transferred by DualVAE using the CIS in CelebA datasets and demonstrate state-of-the-art performance in a multiclass setting.
Generating Diverse High-Resolution Images with VQ-VAE
Authors: Ali Razavi, Aaron van den Oord, Oriol Vinyals
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Decoder, Diversity, Image Generation, Vocal Bursts Intensity Prediction

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before.  We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical. Additionally, this  allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of  VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.
Adversarial Mixup Resynthesizers
Authors: Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019

In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo
Authors: Antonio Moretti, Zizhao Wang, Luhuan Wu, Itsik Pe'er
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Dimensionality Reduction, parameter estimation, Time Series, Time Series Analysis, Variational Inference

The task of recovering nonlinear dynamics and latent structure from a population recording is a challenging problem in statistical neuroscience motivating the development of novel techniques in time series analysis. Recent work has focused on connections between Variational Inference and Sequential Monte Carlo for performing inference and parameter estimation on sequential data. Inspired by this work, we present a framework to develop Smoothed Variational Objectives (SVOs) that condition proposal distributions on the full time-ordered sequence of observations. SVO maintains both expressiveness and tractability by sharing parameters of the transition function between the proposal and target. We apply the method to several dimensionality reduction/expansion tasks and examine the dynamics learned with a quantitative metric. SVO performs favorably against the state of the art.
Revisiting Auxiliary Latent Variables in Generative Models
Authors: Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Date: 2019-03-27 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019

Extending models with auxiliary latent variables is a well-known technique to in-crease model expressivity. Bachman & Precup (2015); Naesseth et al. (2018); Cremer et al. (2017); Domke & Sheldon (2018) show that Importance Weighted Autoencoders (IWAE) (Burda et al., 2015) can be viewed as extending the variational family with auxiliary latent variables. Similarly, we show that this view encompasses many of the recent developments in variational bounds (Maddisonet al., 2017; Naesseth et al., 2018; Le et al., 2017; Yin & Zhou, 2018; Molchanovet al., 2018; Sobolev & Vetrov, 2018). The success of enriching the variational family with auxiliary latent variables motivates applying the same techniques to the generative model. We develop a generative model analogous to the IWAE bound and empirically show that it outperforms the recently proposed Learned Accept/Reject Sampling algorithm (Bauer & Mnih, 2018), while being substantially easier to implement. Furthermore, we show that this generative process provides new insights on ranking Noise Contrastive Estimation (Jozefowicz et al.,2016; Ma & Collins, 2018) and Contrastive Predictive Coding (Oord et al., 2018).
Learning from Samples of Variable Quality
Authors: Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf
Date: 2019-03-11 00:00:00
Proceeding: ICLR Workshop LLD 2019

Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality-versus-quantity trade-off in the learning process.  Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds.   To this end, we introduce “fidelity-weighted learning” (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.
SHREWD: Semantic Hierarchy Based Relational Embeddings For Weakly-Supervised Deep Hashing
Authors: Heikki Arponen, Tom E Bishop
Date: 2019-03-25 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Binarization, Deep Hashing, Retrieval

Using class labels to represent class similarity is a typical approach to training deep hashing systems for retrieval; samples from the same or different classes take binary 1 or 0 similarity values. This similarity does not model the full rich knowledge of semantic relations that may be present between data points. In this work we build upon the idea of using semantic hierarchies to form distance metrics between all available sample labels; for example cat to dog has a smaller distance than cat to guitar. We combine this type of semantic distance into a loss function to promote similar distances between the deep neural network embeddings. We also introduce an empirical Kullback-Leibler divergence loss term to promote binarization and uniformity of the embeddings. We test the resulting SHREWD method and demonstrate improvements in hierarchical retrieval scores using compact, binary hash codes instead of real valued ones, and show that in a weakly supervised hashing setting we are able to learn competitively without explicitly relying on class labels, but instead on similarities between labels.
Heuristics for Image Generation from Scene Graphs
Authors: Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Image Generation, Image Generation from Scene Graphs, Relation

Generating realistic images from scene graphs requires neural networks to be able to reason about object relationships and compositionality. Learning a sufficiently rich representation to facilitate this reasoning is challenging due to dataset limitations. Synthetic scene graphs from COCO only have basic geometric relationships, and Visual Genome scene graphs are replete with missing relations or mislabeled nodes. Existing scene graph to image models have two stages: (1) a scene composition stage, and an (2) image generation stage. In this paper, we propose two methods to improve the intermediate representation of these stages. First, we use visual heuristics to augment relationships between pairs of objects. Second, we introduce a graph convolution-based network to generate a scene graph context representation that enriches the image generation. These contributions significantly improve the scene composition (relation score of 59.8% compared to 51.2%) and image generation (74% versus 64% in mean relation opinion score). Introspection shows that these heuristics are particularly effective in learning differentiated representations for scenes with multiple instances of the same object category. Obtaining accurate and complete scene graph annotations is costly, and our use of heuristics and prior structure to enhance intermediate representations allows our model to compensate for limited or incomplete data.
Unsupervised Functional Dependency Discovery for Data Preparation
Authors: Zhihan Guo, Theodoros Rekatsinas
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: regression

We study the problem of functional dependency (FD) discovery to impose domain knowledge for downstream data preparation tasks. We introduce a framework in which learning functional dependencies corresponds to solving a sparse regression problem. We show that our methods can scale to large data instances with millions of tuples and hundreds of attributes, while recovering true FDs across a diverse array of synthetic datasets, even in the presence of noisy data. Overall, our methods show an average F1 improvement of 2× against state-of-the-art FD discovery methods. Our system also obtains better F1 in downstream data repairing task than manually defined FDs.
Augmented Memory Networks for Streaming-Based Active One-Shot Learning
Authors: Anonymous
Date: 2019-03-20 00:00:00
Tasks: Active Learning, One-Shot Learning, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label or to make a prediction. The goal is to reduce the request rate while at the same time maximize prediction performance. In previous research, reinforcement learning has been used for learning the AL request/prediction strategy. In our work, we propose to equip a reinforcement learning process with memory augmented neural networks, to enhance the one-shot capabilities. Moreover, we introduce Class Margin Sampling (CMS) as an extension of the standard margin sampling to the reinforcement learning setting. This strategy aims to reduce training time and improve sample efficiency in the training process. We evaluate the proposed method on a classification task using empirical accuracy of label predictions and percentage of label requests. The results indicates that the proposed method, by making use of the memory augmented networks and CMS in the training process, outperforms existing baselines.
Enhancing Generalization of First-Order Meta-Learning
Authors: Mirantha Jayathilaka
Date: 2019-03-22 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Few-Shot Image Classification, image-classification, Image Classification, Meta-Learning

In this study we focus on first-order meta-learning algorithms that aim to learn a parameter initialization of a network which can quickly adapt to new concepts, given a few examples. We investigate two approaches to enhance generalization and speed of learning of such algorithms, particularly expanding on the Reptile (Nichol et al., 2018) algorithm. We introduce a novel regularization technique called meta-step gradient pruning and also investigate the effects of increasing the depth of network architectures in first-order meta-learning. We present an empirical evaluation of both approaches, where we match benchmark few-shot image classification results with 10 times fewer iterations using Mini-ImageNet dataset and with the use of deeper networks, we attain accuracies that surpass the current benchmarks of few-shot image classification using Omniglot dataset.
Spatial Broadcast Decoder: A Simple Architecture for Disentangled Representations in VAEs
Authors: Nick Watters, Loic Matthey, Chris P. Burgess, Alexander Lerchner
Date: 2019-03-22 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Decoder, Neural Rendering

We present a neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-“coordinate” channels, and apply a fully convolutional network with 1x1 stride. This provides an architectural prior for dissociating positional from non-positional features in the latent space, yet without providing any explicit supervision to this effect. We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space.  We show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.
Sub-Task Discovery with Limited Supervision: A Constrained Clustering Approach
Authors: Phillip Odom, Aaron Keech, Zsolt Kira
Date: 2019-03-24 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Clustering, Constrained Clustering, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical reinforcement learning captures sub-task information to learn modular policies that can be quickly adapted to new tasks. While hierarchies can be learned jointly with policies, this requires a lot of interaction. Traditional approaches require less data, but typically require sub-task labels to build a task hierarchy. We propose a semi-supervised constrained clustering approach to alleviate the labeling and interaction requirements. Our approach combines limited supervision with an arbitrary set of weak constraints, obtained purely from observations, that is jointly optimized to produce a clustering of the states into sub-tasks. We demonstrate improvement in two visual reinforcement learning tasks.
Train Neural Network by Embedding Space Probabilistic Constraint
Authors: Kaiyuan Chen, Zhanyuan Yin
Date: 2019-03-24 00:00:00
Proceeding: ICLR Workshop LLD 2019

Using higher order knowledge to reduce training data has become a popular research topic. However, the ability for available methods to draw effective decision boundaries is still limited: when training set is small, neural networks will be biased to certain labels. Based on this observation, we consider constraining output probability distribution as higher order domain knowledge. We design a novel algorithm that jointly optimizes output probability distribution on a clustered embedding space to make neural networks draw effective decision boundaries.  While directly applying probability constraint is not effective, users need to provide additional very weak supervisions: mark some batches that have output distribution greatly differ from target probability distribution. We use experiments to empirically prove that our model can converge to an accuracy higher than other state-of-art semi-supervised learning models with less high quality labeled training examples.
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning
Authors: Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata
Date: 2019-03-24 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Few-Shot Learning, Generalized Zero-Shot Learning, Zero-Shot Learning

Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artificial image features. However, learning a shared cross-modal embedding by aligning the latent spaces of modality-specific autoencoders is shown to be promising in (generalized) zero-shot learning. While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier. We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shot as well as on few-shot learning. Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings.
Unifying semi-supervised and robust learning by mixup
Authors: Ryuichiro Hataya, Hideki Nakayama
Date: 2019-03-24 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Learning with noisy labels

Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although these frameworks relax the restriction of supervised learning, they are studied independently. Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown. In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt. Under this framework, we compare recent algorithms for semi-supervised and robust learning. The results suggest that semi-supervised learning outperforms robust learning with noisy labels. We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.
Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies
Authors: James Smith, Seth Baer, Zsolt Kira, Constantine Dovrolis
Date: 2019-03-24 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Continual Learning, Online Clustering

We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time. Given limited labeled data just before inference, those representations can also be associated with specific object types to perform classification. To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM), which loosely models the function of a cortical column in the mammalian brain. Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns and forgetting outliers, and top-down predictions. We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examples per class. STAMs suggest a promising direction to solve the UCL problem without catastrophic forgetting.