147 research outputs found
Transductive Multi-View Zero-Shot Learning
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Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Identifying intents from dialogue utterances forms an integral component of
task-oriented dialogue systems. Intent-related tasks are typically formulated
either as a classification task, where the utterances are classified into
predefined categories or as a clustering task when new and previously unknown
intent categories need to be discovered from these utterances. Further, the
intent classification may be modeled in a multiclass (MC) or multilabel (ML)
setup. While typically these tasks are modeled as separate tasks, we propose
IntenDD, a unified approach leveraging a shared utterance encoding backbone.
IntenDD uses an entirely unsupervised contrastive learning strategy for
representation learning, where pseudo-labels for the unlabeled utterances are
generated based on their lexical features. Additionally, we introduce a
two-step post-processing setup for the classification tasks using modified
adsorption. Here, first, the residuals in the training data are propagated
followed by smoothing the labels both modeled in a transductive setting.
Through extensive evaluations on various benchmark datasets, we find that our
approach consistently outperforms competitive baselines across all three tasks.
On average, IntenDD reports percentage improvements of 2.32%, 1.26%, and 1.52%
in their respective metrics for few-shot MC, few-shot ML, and the intent
discovery tasks respectively.Comment: EMNLP 2023 Finding
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