pwc-archive/papers-with-abstracts huggingface dataset:
GANet: Glyph-Attention Network for Few-Shot Font Generation
Authors: Mingtao Guo, Wei Xiong, Zheng Wang, Yong Tang, Ting Wu
Date: 2021-09-29 00:00:00
Tasks: Decoder, Font Generation

Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles. The time and cost of such task will be greatly reduced if the complete font library can be generated from only a few custom samples. Inspired by font characteristics and global and local attention mechanism Wang et al. (2018), we propose a glyph-attention network (GANet) to tackle this problem. Firstly, a content encoder and a style encoder are trained to extract features as keys and values from a content glyph set and a style glyph set, respectively. Secondly, a query vector generated from a single glyph sample by the query encoder is applied to draw out proper features from the content and style (key, value) pairs via glyph-attention modules. Next, a decoder is used to recover a glyph from the queried features. Lastly, Adversarial losses Goodfellow et al. (2014) with multi-task glyph discriminator are employed to stablize the training process. Experimental results demonstrate that our method is able to create robust results with superior fidelity. Less number of samples are needed and better performance is achieved when compared to the other state-of-the-art few-shot font generation methods, without utilizing supervision on locality such as component, skeleton, or strokes, etc.
There are free lunches
Authors: Zhuoran Xu, Hao liu, Bo Dong
Date: 2021-09-29 00:00:00
Tasks: All

No-Free-Lunch Theorems state that the performance of all algorithms is the same when averaged over all possible tasks. It has been argued that the necessary conditions for NFL are too restrictive to be found in practice. There must be some information for a set of tasks that ensures some algorithms perform better than others. In this paper we propose a novel idea, "There are free lunches" (TAFL) Theorem, which states that some algorithms can achieve the best performance in all possible tasks, in the condition that tasks are given in a specific order. Furthermore, we point out that with the number of solved tasks increasing, the difficulty of solving a new task decreases. We also present an example to explain how to combine the proposed theorem and the existing supervised learning algorithms.
Improving Neural Network Generalization via Promoting Within-Layer Diversity
Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
Date: 2021-09-29 00:00:00
Tasks: Diversity

Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage the diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each unit within the layer to learn a distinctive representation and, thus, to enrich the data representation learned and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network and prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an extensive empirical study confirming that the proposed approach enhances the performance of state-of-the-art neural network models and decreases the generalization gap in multiple tasks.
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty
Authors: Kourosh Meshgi, Maryam Sadat Mirzaei, Satoshi Sekine
Date: 2021-09-29 00:00:00
Tasks: Multi-Task Learning, Q-Learning, Scheduling

Simultaneous training of a multi-task learning network on different domains or tasks is not always straightforward. It could lead to inferior performance or generalization compared to the corresponding single-task networks. To maximally taking advantage of the benefits of multi-task learning, an effective training scheduling method is deemed necessary. Traditional schedulers follow a heuristic or prefixed strategy, ignoring the relation of the tasks, their sample complexities, and the state of the emergent shared features. We proposed a deep Q-Learning Scheduler (QLS) that monitors the state of the tasks and the shared features using a novel histogram of task uncertainty, and through trial-and-error, learns an optimal policy for task scheduling. Extensive experiments on multi-domain and multi-task settings with various task difficulty profiles have been conducted, the proposed method is benchmarked against other schedulers, its superior performance has been demonstrated, and results are discussed.
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level
Authors: Rishi Sonthalia, Raj Rao Nadakuditi
Date: 2021-09-29 00:00:00
Tasks: Denoising

When training a denoising neural network, we show that more data isn’t more beneficial. In fact the generalization error versus number of of training data points is a double descent curve.
Training a network to denoise noisy inputs is the most widely used technique for pre-training deep neural networks. Hence one important question is the effect of scaling the number of training data points. We formalize the question of how many data points should be used by looking at the generalization error for denoising noisy test data. Prior work on computing the generalization error focus on adding noise to target outputs. However, adding noise to the input is more in line with current pre-training practices. In the linear (in the inputs) regime, we provide an asymptotically exact formula for the generalization error for rank 1 data and an approximation for the generalization error for rank r data. We show using our formulas, that the generalization error versus number of data points follows a double descent curve. From this, we derive a formula for the amount of noise that needs to be added to the training data to minimize the denoising error and see that this follows a double descent curve as well.
Sampling Before Training: Rethinking the Effect of Edges in the Process of Training Graph Neural Networks
Authors: Hengyuan Ma, Qi Yang, Bowen Sun, Long Shun, Junkui Li, Jianfeng Feng
Date: 2021-09-29 00:00:00

Graph neural networks (GNN) demonstrate excellent performance on many graph-based tasks; however, they also impose a heavy computational burden when trained on a large-scale graph. Although various sampling methods have been proposed to speed up training GNN by shrinking the scale of the graph during training, they become unavailable if we need to perform sampling before training. In this paper, we quantify the importance of every edge for training in the graph with the extra information they convey in addition to the node features, as inspired by a manifold learning algorithm called diffusion map. Based on this calculation, we propose Graph Diffusion Sampling (GDS), a simple but effective sampling method for shrinking the size of the edge set before training. GDS prefers to sample edges with high importance, and edges dropped by GDS will never be used in the training procedure. We empirically show that GDS preserves the edges crucial for training in a variety of models (GCN, GraphSAGE, GAT, and JKNet). Compared to training on the full graph, GDS can guarantee the performance of the model while only samples a small fraction of the edges.
Should we Replace CNNs with Transformers for Medical Images?
Authors: Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei, Magnus Soderberg, Kevin Smith
Date: 2021-09-29 00:00:00
Tasks: Segmentation

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks. Recently, vision transformers (ViTs) have appeared as a competitive alternative to CNNs, yielding impressive levels of performance in the natural image domain, while possessing several interesting properties that could prove beneficial for medical imaging tasks. In this work, we explore whether it is feasible to switch to transformer-based models in the medical imaging domain as well, or if we should keep working with CNNs - can we trivially replace CNNs with transformers? We consider this question in a series of experiments on several standard medical image benchmark datasets and tasks. Our findings show that, while CNNs perform better if trained from scratch, off-the-shelf vision transformers are on par with CNNs when pretrained on ImageNet in both classification and segmentation tasks. Further, ViTs often outperform their CNN counterparts when pretrained using self-supervision.
Understanding the Generalization Gap in Visual Reinforcement Learning
Authors: Anurag Ajay, Ge Yang, Ofir Nachum, Pulkit Agrawal
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (RL) agents have achieved superhuman performance on several video game suites. However, unlike humans, the trained policies fail to transfer between related games or even between different levels of the same game. Recent works have attempted to reduce this generalization gap using ideas such as data augmentation and learning domain invariant features. However, the transfer performance still remains unsatisfactory. In this work, we use procedurally generated video games to empirically investigate several hypotheses to explain the lack of transfer. We also show that simple auxiliary tasks can improve the generalization of policies. Contrary to the belief that policy adaptation to new levels requires full policy finetuning, we find that visual features transfer across levels, and only the parameters, that use these visual features to predict actions, require finetuning. Finally, to inform fruitful avenues for future research, we construct simple oracle methods that close the generalization gap.
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING
Authors: Shaobo Lin, Xingyu Zeng, Rui Zhao
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Image Classification, Few-Shot Learning, Few-Shot Object Detection, image-classification, Image Classification, Meta-Learning, object-detection, Object Detection

Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations. In this paper, we utilize the idea of meta-learning to integrate two very different streams of few-shot learning, i.e., the episodic meta-learning-based and pre-train finetune-based few-shot learning, and form a unified meta-learning framework. In order to improve the generalization power of our framework, we propose a simple yet effective strategy named meta-dropout, which is applied to the transferable knowledge generalized from base categories to novel categories. The proposed strategy can effectively prevent neural units from co-adapting excessively in the meta-training stage. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.
Distributional Generalization: Structure Beyond Test Error
Authors: Preetum Nakkiran, Yamini Bansal
Date: 2021-09-29 00:00:00

Classifiers in machine learning are often reduced to single dimensional quantities, such as test error or loss. Here, we initiate a much richer study of classifiers by considering the entire joint distribution of their inputs and outputs. We present both new empirical behaviors of standard classifiers, as well as quantitative conjectures which capture these behaviors. Informally, our conjecture states: the output distribution of an interpolating classifier matches the distribution of true labels, when conditioned on certain subgroups of the input space. For example, if we mislabel 30% of dogs as cats in the train set of CIFAR-10, then a ResNet trained to interpolation will in fact mislabel roughly 30% of dogs as cats on the *test set* as well, while leaving other classes unaffected. This conjecture has implications for the theory of overparameterization, scaling limits, implicit bias, and statistical consistency. Further, it can be seen as a new kind of generalization, which goes beyond measuring single-dimensional quantities to measuring entire distributions.
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Authors: Jack Parker-Holder, Minqi Jiang, Michael D Dennis, Mikayel Samvelyan, Jakob Nicolaus Foerster, Edward Grefenstette, Tim Rocktäschel
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (RL) has recently produced impressive results in a series of settings such as games and robotics. However, a key challenge that limits the utility of RL agents for real-world problems is the agent's ability to generalize to unseen variations (or levels). To train more robust agents, the field of Unsupervised Environment Design (UED) seeks to produce a curriculum by updating both the agent and the distribution over training environments. Recent advances in UED have come from promoting levels with high regret, which provides theoretical guarantees in equilibrium and empirically has been shown to produce agents capable of zero-shot transfer to unseen human-designed environments. However, current methods require either learning an environment-generating adversary, which remains a challenging optimization problem, or curating a curriculum from randomly sampled levels, which is ineffective if the search space is too large. In this paper we instead propose to evolve a curriculum, by making edits to previously selected levels. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), produces levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior works, while outperforming them empirically when transferring to complex out-of-distribution environments.
Differential Privacy with Manifold Data Dependency
Authors: Lei Wang, Deming Yuan, Guodong Shi
Date: 2021-09-29 00:00:00

In this paper, we study dataset processing mechanisms generated by linear queries in the presence of manifold data dependency. Specifically, the input data are assumed to lie in an affine manifold as prior knowledge known to adversaries. First of all, we show such manifold data dependency may have a significant impact on the privacy levels compared to the case with the manifold constraint being absent.  We establish necessary and sufficient conditions on the possibility of achieving differential privacy  via structured noise injection mechanisms where  non i.i.d. Gaussian or Laplace noises are calibrated into dataset. Next, in light of these conditions, procedures are developed  by which a prescribed privacy budget  can be tightly reached with a matching noise level.  Finally, we show that the  framework has immediate applications  in  differentially private cloud-based control, where the manifold data dependency arises naturally from the system dynamics, and the proposed theories and procedures become effective tools in evaluating privacy levels and in the design of provably useful  algorithms.
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization
Authors: Wenyu Zhang, Chuan-Sheng Foo, Mohamed Ragab
Date: 2021-09-29 00:00:00
Tasks: Domain Generalization, Representation Learning, Time Series, Time Series Analysis, Time Series Classification

Domain generalization aims to learn models robust to domain shift, with limited source domains at training and without any access to target domain samples except at test time. Current domain alignment methods seek to extract features invariant across all domains, but do not consider inter-domain relationships. In this paper, we propose a novel representation learning methodology for time series classification that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we view a domain shift as a form of data transformation that preserves labels but not necessarily class relationships, and we regularize the predicted class relationships to be shared only by closely-related domains instead of all domains to prevent negative transfer. We conduct comprehensive experiments on two public real-world datasets. The proposed method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods.
Resolving Training Biases via Influence-based Data Relabeling
Authors: Shuming Kong, Yanyan Shen, Linpeng Huang
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

The performance of supervised learning methods easily suffers from the training bias issue caused by train-test distribution mismatch or label noise. Influence function is a  technique that estimates the impacts of a training sample on the model’s predictions. Recent studies on \emph{data resampling} have employed influence functions to identify \emph{harmful} training samples that will degrade model's test performance. They have shown that discarding or downweighting the identified harmful training samples is an effective way to resolve training biases. In this work, we move one step forward and propose an influence-based relabeling framework named RDIA for reusing harmful training samples toward better model performance. To achieve this, we use influence functions to estimate how relabeling a training sample would affect model's test performance and further develop a novel relabeling function R. We theoretically prove that applying R to relabel harmful training samples allows the model to achieve lower test loss than simply discarding them for any classification tasks using cross-entropy loss. Extensive experiments on ten real-world datasets demonstrate RDIA outperforms the state-of-the-art data resampling methods and improves model's robustness against label noise.
Task-Agnostic Graph Neural Explanations
Authors: Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward W Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji
Date: 2021-09-29 00:00:00

Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph structured data. Existing learning-based GNN explanation approaches are task-specific in training and hence suffer from crucial drawbacks. Specifically, they are incapable of producing explanations for a multitask prediction model with a single explainer. They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future down-stream tasks. To address these limitations,  we propose a Task-Agnostic Graph Neural Explainer (TAGE) trained under self-supervision without knowledge about downstream tasks.   TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models. Our extensive experiments show that TAGE can significantly speed up the explanation efficiency while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches.
Treatment effect estimation with confounder balanced instrumental variable regression
Authors: Anpeng Wu, Kun Kuang, Fei Wu
Date: 2021-09-29 00:00:00
Tasks: regression

This paper considers the challenge of estimating treatment effects from observational data in the presence of unmeasured confounders. A popular way to address this challenge is to utilize an instrumental variable (IV) for two-stage regression, i.e., 2SLS and variants, but they need to assume the additive separability of noise and are limited to the linear setting. Recently, many nonlinear IV regression variants were proposed by regressing the treatment with IVs and confounders in the first stage, leading to confounding bias between the predicted treatment and outcome in the second stage. In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders with IV regression and reduce the bias from the observed confounders by balancing for treatment effect estimation. Specifically, CB-IV algorithm consists of three main modules: (1) treatment regression: regressing the treatment with IVs and confounders like previous nonlinear IV methods for removing the confounding from unmeasured confounders; (2) confounder balancing: learning a balanced representation of confounders to eliminate the bias induced by the observed confounders  (3) outcome regression: regressing the outcome with the predicted treatment and the balanced confounders representation for treatment effect estimation. To the best of our knowledge, this is the first work to combine confounder balancing in IV regression for treatment effect estimation. Moreover, we theoretically prove that CB-IV algorithm is also effective even without the additive separability assumption on noise. Extensive experiments demonstrate that the CB-IV algorithm outperforms the state-of-the-art methods, including IV regression and confounder balancing methods, for treatment effect estimation.
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning
Authors: Jiechuan Jiang, Zongqing Lu
Date: 2021-09-29 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning could learn effective policies from a fixed dataset, which is promising in real-world applications. However, in offline decentralized multi-agent reinforcement learning, due to the discrepancy between the behavior policy and learned policy, the transition dynamics in offline experiences do not accord with the transition dynamics in online execution, which creates severe errors in value estimates, leading to uncoordinated and suboptimal policies. One way to overcome the transition bias is to bridge offline training and online tuning. However, considering both deployment efficiency and sample efficiency, we could only collect very limited online experiences, making it insufficient to use merely online data for updating the agent policy. To utilize both offline and online experiences to tune the policies of agents, we introduce online transition correction (OTC) to implicitly correct the biased transition dynamics by modifying sampling probabilities. We design two types of distances, i.e., embedding-based and value-based distance, to measure the similarity between transitions, and further propose an adaptive rank-based prioritization to sample transitions according to the transition similarity. OTC is simple yet effective to increase data efficiency and improve agent policies in online tuning. Empirically, we show that OTC outperforms baselines in a variety of tasks.