Zero Shot Learning with the Isoperimetric Loss

We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. We use a pre-trained deep neural network model as a visual representation of image data, a Word2Vec embedding of class labels, and linear maps between the visual and semantic embedding spaces. However, the spaces themselves are not linear, and we postulate the sample embedding to be populated by noisy samples near otherwise smooth manifolds. We exploit the graph structure defined by the sample points to regularize the estimates of the manifolds by inferring the graph connectivity using a generalization of the isoperimetric inequalities from Riemannian geometry to graphs. Surprisingly, this regularization alone, paired with the simplest baseline model, outperforms the state-of-the-art among fully automated methods in zero-shot learning benchmarks such as AwA and CUB. This improvement is achieved solely by learning the structure of the underlying spaces by imposing regularity.Comment: Accepted to AAAI-2

Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their interactions with the environment and other organisms. However, traditional observation methods are labor- intensive. Most prior work on machine learning for bird song is not applicable to real-world acoustic monitoring, because it assumes recordings contain only a single species of bird, while recordings typically contain multiple simultaneously vocalizing birds. We propose to use the multi-instance multi-label (MIML) framework in machine learning for the species classification problem, where the dataset is viewed as a collection of bags of instances paired with sets of labels. Furthermore, we formalize MIML instance annotation, where the goal is to predict instance labels while learning only from bag label sets. We develop the first MIML representation for audio, and several new algorithms for MIML instance annotation based on support vector machines or classifier chains. The proposed methods classify either the set of species present in a recording, or individual calls, while learning only from recordings paired with a set of species. This form of training data requires less human effort to obtain than individually labeled calls. These methods are successfully applied to audio collected in the field which included multiple simultaneously vocalizing species. The proposed algorithms for MIML classification are general, and are also applied to object recognition in images

Unsupervised Domain Adaptation using Graph Transduction Games

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.Comment: Oral IJCNN 201

Ontology-based annotation of paintings with artistic concepts

Ph.DDOCTOR OF PHILOSOPH