Supervised Knowledge May Hurt Novel Class Discovery Performance

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given that most existing literature focuses primarily on utilizing supervised knowledge from a labeled set at the methodology level, this paper considers the question: Is supervised knowledge always helpful at different levels of semantic relevance? To proceed, we first establish a novel metric, so-called transfer flow, to measure the semantic similarity between labeled/unlabeled datasets. To show the validity of the proposed metric, we build up a large-scale benchmark with various degrees of semantic similarities between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. The results based on the proposed benchmark show that the proposed transfer flow is in line with the hierarchical class structure; and that NCD performance is consistent with the semantic similarities (measured by the proposed metric). Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance. Specifically, using supervised information from a low-similarity labeled set may lead to a suboptimal result as compared to using pure self-supervised knowledge. These results reveal the inadequacy of the existing NCD literature which usually assumes that supervised knowledge is beneficial. Finally, we develop a pseudo-version of the transfer flow as a practical reference to decide if supervised knowledge should be used in NCD. Its effectiveness is supported by our empirical studies, which show that the pseudo transfer flow (with or without supervised knowledge) is consistent with the corresponding accuracy based on various datasets. Code is released at https://github.com/J-L-O/SK-Hurt-NCDComment: TMLR 2023 accepted paper. arXiv admin note: substantial text overlap with arXiv:2209.0912

Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

A connectionist account of the emergence of the literal-metaphorical-anomalous distinction in young children

We present the first developmental computational model of metaphor comprehension, which seeks to relate the emergence of a distinction between literal and non-literal similarity in young children to the development of semantic representations. The model gradually learns to distinguish literal from metaphorical semantic juxtapositions as it acquires more knowledge about the vehicle domain. In accordance with Keil (1986), the separation of literal from metaphorical comparisons is found to depend on the maturity of the vehicle concept stored within the network. The model generates a number of explicit novel predictions

Fostering shared knowledge with active graphical representation in different collaboration scenarios

This study investigated how two types of graphical representation tools influence the way in which learners use shared and unshared knowledge resources in two different collaboration scenarios, and how learners represent and transfer shared knowledge under these different conditions. Moreover, the relation between the use of knowledge resources, representation, and the transfer of shared knowledge was analyzed. The type of graphical representation (content-specific vs. content-unspecific) and the collaboration scenario (video conferencing vs. face-to-face) were varied. 64 university students participated. Results show that the learning partners converged in their profiles of resource use. With the content-specific graphical representation, learners used more appropriate knowledge resources. Learners in the computer-mediated scenarios showed a greater bandwidth in their profiles of resource use. A relation between discourse and outcomes could be shown for the transfer but not for the knowledge representation aspectIn dieser Studie werden die Wirkungen von verschiedenen Arten graphischer Repräsentation auf die Nutzung geteilter und ungeteilter Wissensressourcen in zwei verschiedenen Kooperationsszenarien untersucht. Des Weiteren wird analysiert, wie Lernende geteiltes und ungeteiltes Wissen unter diesen verschiedenen Bedingungen repräsentieren und transferieren. Schließlich wird die Beziehung zwischen der Nutzung von Wissensressourcen auf der einen Seite sowie der Repräsentation und dem Transfer geteilten Wissens auf der anderen Seite geprüft. Mit der Art der graphischen Repräsentation (inhaltsspezifisch vs. inhaltsunspezifisch) und dem Kooperationsszenario (Videokonferenz vs. face-to-face) werden zwei Faktoren experimentell variiert. 64 Studierende nahmen an der Studie teil. Ergebnisse zeigen, dass die Lernpartner in ihren Profilen der Ressourcennutzung konvergierten. Lernende, die durch die inhaltsspezifische graphische Repräsentation unterstützt wurden, verwendeten angemessenere Wissensressourcen. Lernende in den computervermittelten Szenarien weisen eine größere Bandbreite in ihren Profilen der Ressourcennutzung auf. Eine direkte Wirkung vom Diskurs der Lernenden auf die Entwicklung geteilten Wissens konnte für den Transfer, aber nicht für die Wissensrepräsentation gezeigt werde

The phonetics of second language learning and bilingualism

This chapter provides an overview of major theories and findings in the field of second language (L2) phonetics and phonology. Four main conceptual frameworks are discussed and compared: the Perceptual Assimilation Model-L2, the Native Language Magnet Theory, the Automatic Selection Perception Model, and the Speech Learning Model. These frameworks differ in terms of their empirical focus, including the type of learner (e.g., beginner vs. advanced) and target modality (e.g., perception vs. production), and in terms of their theoretical assumptions, such as the basic unit or window of analysis that is relevant (e.g., articulatory gestures, position-specific allophones). Despite the divergences among these theories, three recurring themes emerge from the literature reviewed. First, the learning of a target L2 structure (segment, prosodic pattern, etc.) is influenced by phonetic and/or phonological similarity to structures in the native language (L1). In particular, L1-L2 similarity exists at multiple levels and does not necessarily benefit L2 outcomes. Second, the role played by certain factors, such as acoustic phonetic similarity between close L1 and L2 sounds, changes over the course of learning, such that advanced learners may differ from novice learners with respect to the effect of a specific variable on observed L2 behavior. Third, the connection between L2 perception and production (insofar as the two are hypothesized to be linked) differs significantly from the perception-production links observed in L1 acquisition. In service of elucidating the predictive differences among these theories, this contribution discusses studies that have investigated L2 perception and/or production primarily at a segmental level. In addition to summarizing the areas in which there is broad consensus, the chapter points out a number of questions which remain a source of debate in the field today.https://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHAccepted manuscriptAccepted manuscrip