pwc-archive/papers-with-abstracts huggingface dataset:
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification
Authors: Wenming Cao, Qifan Liu, Guang Liu, Zhihai He
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification, Prediction

We construct a prime-dual network structure for few-shot learning which establishes a commutative relationship between the support set and the query set, as well as a new self- supervision constraint for highly effective few-shot learning. Specifically, the prime network performs the forward label prediction of the query set from the support set, while the dual network performs the reverse label prediction of the support set from the query set. This forward and reserve prediction process with commutated support and query sets forms a label prediction loop and establishes a self-supervision constraint between the ground-truth labels and their predicted values. This unique constraint can be used to significantly improve the training performance of few-shot learning through coupled prime and dual network training. It can be also used as an objective function for optimization during the testing stage to refine the query label prediction results. Our extensive experimental results demonstrate that the proposed self-supervised commutative learning and optimization outperforms existing state-of the-art few-shot learning methods by large margins on various benchmark datasets.
AutoMO-Mixer: An automated multi-objective multi-layer perspecton Mixer model for medical image based diagnosis
Authors: Xi Chen, Jiahuan Lv, Xuanqing Mou, Zhiguo Zhou
Date: 2021-09-29 00:00:00
Tasks: Bayesian Optimization, Medical Image Analysis, Sensitivity, Specificity

Medical image based diagnosis is one of the most challenging things which is vital to human life. Accurately identifying the patient's status through medical images plays an important role in treatment of diseases. Deep learning has achieved great success in medical image analysis. Particularly, Convolutional neural network CNN) can obtain promising performance by learning the features in a supervised way. However, since there are too many parameters to train, CNN always requires a large scale dataset to feed, while it is very difficult to collect the required amount of patient images for a particular clinical problem. Recently, MLP-Mixer (Mixer) which is developed based multiple layer perceptron (MLP) was proposed, in which the number of training parameters is greatly decreased by removing convolutions in the architecture, while it can achieve the similar performance with CNN. Furthermore, obtaining the balanced outcome between sensitivity and specificity is of great importance in patient's status identification. As such, a new automated multi-objective Mixer (AutoMO-Mixer) model was developed in this study.  In AutoMO-Mixer, sensitivity and specificity were considered as the objective functions simultaneously to train the model and a Pareto-optimal Mixer model set can be obtained in the training stage. Additionally, since there are several hyperparameters to train, the Bayesian optimization was introduced. To obtain a more reliable results in testing stage, the final output was obtained by fusing the output probabilities of Pareto optimal models through  the evidence reasoning (ER) approach. The experimental study demonstrated that AutoMO-Mixer can obtain better performance compared with Mixer and CNN.
Improving Adversarial Defense with Self-supervised Test-time Fine-tuning
Authors: Zhichao Huang, Chen Liu, Mathieu Salzmann, Sabine Süsstrunk, Tong Zhang
Date: 2021-09-29 00:00:00
Tasks: Adversarial Defense

Although adversarial training and its variants currently constitute the most effective way to achieve robustness against adversarial attacks, their poor generalization limits their performance on the test samples. In this work, we propose to improve the generalization and robust accuracy of adversarially-trained networks via self-supervised test-time fine-tuning. To this end, we introduce a meta adversarial training method to find a good starting point for test-time fine-tuning. It incorporates the test-time fine-tuning procedure into the training phase and strengthens the correlation between the self-supervised and classification tasks. The extensive experiments on CIFAR10, STL10 and Tiny ImageNet using different self-supervised tasks show that our method consistently improves the robust accuracy under different attack strategies for both the white-box and black-box attacks.
Invariance-Guided Feature Evolution for Few-Shot Learning
Authors: Wenming Cao, Zhineng Zhao, Qifan Liu, Zhihai He
Date: 2021-09-29 00:00:00
Tasks: domain classification, Few-Shot Learning, image-classification, Image Classification

Few-shot learning (FSL) aims to characterize the inherent visual relationship between support and query samples which can be well generalized to unseen classes so that we can accurately infer the labels of query samples from very few support samples. We observe that, in a successfully learned FSL model, this visual relationship and the learned features of the query samples should remain largely invariant across different configurations of the support set. Driven by this observation, we propose to construct a feature evolution network with an ensemble of few-shot learners evolving along different configuration dimensions. We choose to study two major parameters that control the support set configuration: the number of labeled samples per class (called shots) and the percentage of training samples (called partition) in the support set. Based on this network, we characterize and track the evolution behavior of learned query features across different shots-partition configurations, which will be minimized by a set of invariance loss functions during the training stage. Our extensive experimental results demonstrate that the proposed invariance-guided feature evolution (IGFE) method significantly improves the performance and generalization capability of few-shot learning and outperforms the state-of-the-art methods by large margins, especially in cross-domain classification tasks for generalization capability test. For example, in the cross-domain test on the fine-grained CUB image classification task, our method has improved the classification accuracy by more than 5%.
Variability of Neural Networks and Han-Layer: A Variability-Inspired Model
Authors: Yueyao Yu, Yin Zhang
Date: 2021-09-29 00:00:00

What makes an artificial neural network easier to train or to generalize better than its peers?  We introduce a notion of variability to view such issues under the setting of a fixed number of parameters which is, in general, a dominant cost-factor.  Experiments verify that variability correlates positively to the number of activations and negatively to a phenomenon called Collapse to Constants, which is related but not identical to vanishing gradient.  Further experiments on stylized problems show that variability is indeed a key performance indicator for fully-connected neural networks.  Guided by variability considerations, we propose a new architecture called Householder-absolute neural layers, or Han-layers for short, to build high variability networks with a guaranteed immunity to gradient vanishing or exploding.
On small stylized models, Han-layer networks exhibit a far superior generalization ability over fully-connected networks.  Extensive empirical results demonstrate that, by judiciously replacing fully-connected layers in large-scale networks such as MLP-Mixers, Han-layers can greatly reduce the number of model parameters while maintaining or improving generalization performance.  We will also briefly discuss current limitations of the proposed Han-layer architecture.
Effect of scale on catastrophic forgetting in neural networks
Authors: Vinay Venkatesh Ramasesh, Aitor Lewkowycz, Ethan Dyer
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Continual Learning

Catastrophic forgetting presents a challenge in developing deep learning models capable of continual learning, i.e. learning tasks sequentially. Recently, both computer vision and natural-language processing have witnessed great progress through the use of large-scale pretrained models. In this work, we present an empirical study of catastrophic forgetting in this pretraining paradigm.
Our experiments indicate that large, pretrained ResNets and Transformers are significantly more resistant to forgetting than randomly-initialized, trained-from-scratch models; this robustness systematically improves with scale of both model and pretraining dataset size.
We take initial steps towards characterizing what aspect of model representations allows them to perform continual learning so well, finding that in the pretrained models, distinct class representations grow more orthogonal with scale.  Our results suggest that, when possible, scale and a diverse pretraining dataset can be useful ingredients in mitigating catastrophic forgetting.
From Graph Local Embedding to Deep Metric Learning
Authors: Bing-Jhang Lin, Ding-Jie Chen, He-Yen Hsieh, Tyng-Luh Liu
Date: 2021-09-29 00:00:00
Tasks: Metric Learning, Retrieval

Deep metric learning continues to play a crucial role in many computer vision applications, while its various mining and weighting strategies have been extensively investigated. Techniques based on pairwise learning often use excessive random sampling and end up in slow convergence and model degradation. Further, neural network approaches mostly employ MLP layers for metric learning. The tactic can indeed be thought of as graph convolutions with only self-connections, indicating that local neighborhood relationships are neglected. We comprehensively identify the missing neighborhood  relationships issue of conventional embedding and propose a novel approach, termed as Graph Local Embedding (GLE), to deep metric learning. Our method explores the local relationships and draws on the graph convolution networks to construct a discriminative mapping for embedding learning. The strategy can enhance metric learning by exploring the manifold-to-manifold relationships. By focusing on an essential variety of neighboring relations within GLE, burdens of redundant pairs can be substantially eased, and the context of each encoded data is greatly enriched. We demonstrate in the experiments that coupling GLE with existing metric learning techniques can yield impressive performance gains on popular benchmark datasets for fine-grained retrieval.
Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games?
Authors: Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael Jordan
Date: 2021-09-29 00:00:00
Tasks: Reinforcement Learning (RL)

We study multi-player general-sum Markov games with one of the players designated as the leader and the rest regarded as the followers. In particular, we focus on the class of games where the state transitions are only determined by the leader's action while the actions of all the players determine their immediate rewards.  For such a game, our goal is to find the Stackelberg-Nash equilibrium (SNE), which is a policy pair $(\pi^*, \nu^*)$ such that (i) $\pi^*$ is the optimal policy for the leader when the followers always play their best response, and (ii) $\nu^*$ is the best response policy of the followers, which is a Nash equilibrium of the followers' game induced by $\pi^*$. We develop sample efficient reinforcement learning (RL) algorithms for solving SNE for both the online and offline settings. Respectively, our algorithms are optimistic and pessimistic variants of least-squares value iteration and are readily able to incorporate function approximation for handling large state spaces. Furthermore, for the case with linear function approximation,  we prove that our algorithms achieve sublinear regret and suboptimality under online and offline setups respectively. To our best knowledge, we establish the first provably efficient RL algorithms for solving SNE in general-sum Markov games with leader-controlled state transitions.
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Authors: Rafid Mahmood, Sanja Fidler, Marc T Law
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Active Learning

Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in deep learning forces most sample selection strategies to employ efficient heuristics. This paper introduces an integer optimization problem for selecting a core set that minimizes the discrete Wasserstein distance from the unlabeled pool. We demonstrate that this problem can be tractably solved with a Generalized Benders Decomposition algorithm. Our strategy uses high-quality latent features that can be obtained by unsupervised learning on the unlabeled pool. Numerical results on several data sets show that our optimization approach is competitive with baselines and particularly outperforms them in the low budget regime where less than one percent of the data set is labeled.
Evolving Neural Update Rules for Sequence Learning
Authors: Karol Gregor, Peter Conway Humphreys
Date: 2021-09-29 00:00:00

We consider the problem of searching, end to end, for effective weight and activation update rules governing online learning of a recurrent network on problems of character sequence memorisation and prediction. We experiment with a number of functional forms and find that the performance depends on them significantly. We find update rules that allow us to scale to a much larger number of recurrent units and much longer sequence lengths than has been achieved with this approach previously. We also find that natural evolution strategies significantly outperforms meta-gradients on this problem, aligning with previous studies suggesting that such evolutionary strategies are more robust than gradient back-propagation over sequences with thousands(s) of steps.
Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI
Authors: Kai Chen, Yongqiang Ma, Mingyang Sheng, Nanning Zheng
Date: 2021-09-29 00:00:00
Tasks: Decoder, Image Reconstruction

The reconstruction of visual stimulus images from functional Magnetic Resonance Imaging (fMRI) has received extensive attention in recent years, which provides a possibility to interpret the human brain. Due to the high-dimensional and high-noise characteristics of fMRI data, how to extract stable, reliable and useful information from fMRI data for image reconstruction has become a challenging problem. Inspired by the mechanism of human visual attention, in this paper, we propose a novel method of reconstructing visual stimulus images, which first decodes the distribution of visual attention from fMRI, and then reconstructs the visual images guided by visual attention. We define visual attention as foreground attention (F-attention). Because the human brain is strongly wound into sulci and gyri, some spatially adjacent voxels are not connected in practice. Therefore, it is necessary to consider the global information when decoding fMRI, so we introduce the self-attention module for capturing global information into the process of decoding F-attention. In addition, in order to obtain more loss constraints in the training process of encoder-decoder, we also propose a new training strategy called Loop-Enc-Dec. The experimental results show that the F-attention decoder decodes the visual attention from fMRI successfully, and the Loop-Enc-Dec guided by F-attention can also well reconstruct the visual stimulus images.
Building the Building Blocks: From Simplification to Winning Trees in Genetic Programming
Authors: Lucija Planinić, Marko Đurasević, Stjepan Picek, Domagoj Jakobovic
Date: 2021-09-29 00:00:00
Tasks: Symbolic Regression

Genetic Programming (GP) represents a powerful paradigm in diverse real-world applications. While GP can reach optimal (or at least ``good-enough'') solutions for many problems, such solutions are not without deficiencies. A frequent issue stems from the representation perspective where GP evolves solutions that contain unnecessary parts, known as program bloat.
This paper first investigates a combination of deterministic and random simplification to simplify the solutions while having a (relatively) small influence on the solution fitness. Afterward, we use the solutions to extract their subtrees, which we denote as winning trees. The winning trees can be used to initialize the population for the new GP run and result in improved convergence and fitness, provided some conditions on the size of solutions and winning trees are fulfilled. To experimentally validate our approach, we consider several synthetic benchmark problems and real-world symbolic regression problems.
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis
Authors: Anton Plaksin, Stepan Martyanov
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the most effective continuous deep reinforcement learning algorithms is normalized advantage functions (NAF). The main idea of NAF consists in the approximation of the Q-function by functions quadratic with respect to the action variable. This idea allows to apply the algorithm to continuous reinforcement learning problems, but on the other hand, it brings up the question of classes of problems in which this approximation is acceptable. The presented paper describes one such class. We consider reinforcement learning problems obtained by the time-discretization of certain optimal control problems. Based on the idea of NAF, we present a new family of quadratic functions and prove its suitable approximation properties. Taking these properties into account, we provide several ways to improve NAF. The experimental results confirm the efficiency of our improvements.
Explaining Knowledge Graph Embedding via Latent Rule Learning
Authors: Wen Zhang, Mingyang Chen, Zezhong Xu, Yushan Zhu, Huajun Chen
Date: 2021-09-29 00:00:00
Tasks: Graph Embedding, Knowledge Distillation, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction, Prediction, Representation Learning

Knowledge Graph Embeddings (KGEs) embed entities and relations into continuous vector space following certain assumption, and are a powerful tools for representation learning of knowledge graphs. However, following vector space assumptions makes KGE a one step reasoner that directly predict final results without reasonable multi-hop reasoning steps. Thus KGEs are black-box models and explaining predictions made by KGEs remains unsolved. In this paper, we propose KGExplainer, the first general approach of providing explanations for predictions from KGE models.  KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation. For explanation, KGExplainer outputs a ranked list of rules for each relation. Experiments on benchmark datasets with two target KGEs show that our approach is faithfulness to replicate KGEs behaviors for link prediction and is good at outputting quality rules  for effective explanations.
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels
Authors: Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang
Date: 2021-09-29 00:00:00
Tasks: Graph Mining, Graph Neural Network, Node Classification

Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels. However, it is expensive to maintain the label quality for real-world graph datasets, which presents huge challenges for the learning algorithm to keep a good generalization ability. In this paper, we propose a novel robust learning objective dubbed pairwise interactions (PI) for the model, such as Graph Neural Network (GNN) to combat against noisy labels. Unlike classic robust training approaches that operate on the pointwise interactions between node and class label pairs, PI explicitly forces the embeddings for node pairs that hold a positive PI label to be close to each other, which can be applied to both labeled and unlabeled nodes. We design several instantiations for the PI labels based on the graph structure as well as node class labels, and further propose a new uncertainty-aware training technique to mitigate the negative effect of the sub-optimal PI labels. Extensive experiments on different datasets and GNN architectures demonstrate the effectiveness of PI, which also brings a promising improvement over the state-of-the-art methods.
Inducing Reusable Skills From Demonstrations with Option-Controller Network
Authors: Siyuan Zhou, Yikang Shen, Yuchen Lu, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan
Date: 2021-09-29 00:00:00

Humans can decompose previous experiences into skills and reuse them to enable fast learning in the future. Inspired by this process, we propose a new model called Option-Controller Network (OCN), which is a bi-level recurrent policy network composed of a high-level controller and a pool of low-level options. The options are disconnected from any task-specific information to model task-agnostic skills.
The controller use options to solve a given task, and it calls one option at a time and waits until the option return. With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime. In experiments, we first perform behavior cloning from unstructured demonstrations coming from different tasks. We then freeze the learned options and learn a new controller with an RL algorithm to solve a new task. Extensive results on discrete and continuous environments show that OCN can jointly learn to decompose unstructured demonstrations into skills and model each skill with separate options. The learned options provide a good temporal abstraction, allowing OCN to quickly transfer to tasks with a novel combination of learned skills even with sparse reward, while previous methods either suffer from the delayed reward problem due to the lack of temporal
abstraction or a complicated option controlling mechanism that increases the complexity of exploration.