Audio events are quite often overlapping in nature, and more prone to noise than visual signals. There has been increasing evidence for the superior performance of representations learned using sparse dictionaries for applications like audio denoising and speech enhancement. This paper concentrates on modifying the traditional reconstructive dictionary learning algorithms, by incorporating a discriminative term into the objective function in order to learn class-specific adversarial dictionaries that are good at representing samples of their own class at the same time poor at representing samples belonging to any other class. We quantitatively demonstrate the effectiveness of our learned dictionaries as a stand-alone solution for both binary as well as multi-class audio classification problems.
In this paper, we will be learning class specific dictionaries that are adversarial in nature. By adversarial we mean each of these class specific dictionaries is learned in such a manner that apart from doing a good job at representing samples of their own class, they also have to do a poor job at representing samples belonging to any other class. This allows us to learn a structured dictionary where atoms have correspondence to the class labels so that the reconstruction error associated with each class can be used for classification and hence can act as a stand-alone classifier as opposed to @cite_23 @cite_2 @cite_17 @cite_10 @cite_14 , which relies on expensive classifiers like RBF Kernal SVM trained on sparse coefficients for making final predictions. We propose a direct method to train these dictionaries in a multi-class setting, unlike @cite_12 @cite_4 , that deal with multiclass problem into multiple independent binary classification tasks and these might not scale well for large number of classes and are often computationally expensive. Our method is unique as compared to previous approaches as they are designed to capture inter-class differences in a much better manner even for multi-class problem instances.
@cite_14 @cite_4 @cite_23 @cite_2 @cite_10 @cite_12 @cite_17
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We consider Multiclass and Multilabel classification with extremely large number of classes, of which only few are labeled to each instance. In such setting, standard methods that have training, prediction cost linear to the number of classes become intractable. State-of-the-art methods thus aim to reduce the complexity by exploiting correlation between labels under assumption that the similarity between labels can be captured by structures such as low-rank matrix or balanced tree. However, as the diversity of labels increases in the feature space, structural assumption can be easily violated, which leads to degrade in the testing performance. In this work, we show that a margin-maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor. We thus propose a Fully-Corrective Block-Coordinate Frank-Wolfe (FC-BCFW) algorithm that exploits both primal and dual sparsity to achieve a complexity sublinear to the number of primal and dual variables. A bi-stochastic search method is proposed to further improve the efficiency. In our experiments on both Multiclass and Multilabel problems, the proposed method achieves significant higher accuracy than existing approaches of Extreme Classification with very competitive training and prediction time. Abstract Automatically extracting semantic information about word mean-ing and document topic from text typically involves an extensivenumber of classes. Such classes may represent prede ned wordsenses, topics or document categories and are often organized in ataxonomy. Thelatterencodesimportantinformation,whichshouldbe exploited in learning classi ers from labeled training data. Tothat extent, this paper presents an extension of multiclass SupportVector Machine learning which can incorporate prior knowledgeabout class relationships. The latter can be encoded in the form ofclass attributes, similarities between classes or even a kernel func-tion de ned over the set of classes. The paper also discusses howto specify and optimize meaningful loss functions based on the rel-ative position of classes in the taxonomy. We include experimentalresults for text categorization and for word sense classi cation. 1 Introduction Manyreal-worldclassi cationtasksaremulticlassproblemsinvolvinglargenumbersof classes. This is in particular true for application domains like information re-trieval and natural language processing, where classes may correspond to documentcategories or word senses: several thousand or even tens of thousands of classes arenot uncommon. For instance, the International Patent Classi cation (IPC) scheme[8] consists of approximately 69,000 classes (called groups) that are used to catego-rize patent documents and WordNet 2.0 [3] consists of almost 80,000 word senses(called synsets) de ned by lexicographers to classify the meaning of English nouns.Multiclass problems of this scale pose a severe challenge for learning algorithms andclassi cation accuracies obtained by even the best classi cation methods are oftendisappointingly poor. In this paper, we address the incremental classifier learning problem, which suffers from catastrophic forgetting. The main reason for catastrophic forgetting is that the past data are not available during learning. Typical approaches keep some exemplars for the past classes and use distillation regularization to retain the classification capability on the past classes and balance the past and new classes. However, there are four main problems with these approaches. First, the loss function is not efficient for classification. Second, there is unbalance problem between the past and new classes. Third, the size of pre-decided exemplars is usually limited and they might not be distinguishable from unseen new classes. Forth, the exemplars may not be allowed to be kept for a long time due to privacy regulations. To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation. We believe that the data generated by GANs have much less privacy issues than real images because GANs do not directly copy any real image patches. We evaluate the proposed method on CIFAR-100, Flower-102, and MS-Celeb-1M-Base datasets and extensive experiments demonstrate the effectiveness of our method. We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network. However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes. This can lead to ambiguous features being generated near class boundaries, reducing target classification accuracy. We propose a novel approach, Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain. Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classifier network. The generator then learns to avoid these areas of the feature space and thus creates better features. We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvement over the state of the art. We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.