Zero-shot Learning with Semantic Output Codes

By Mark Palatucci, Dean A. Pomerleau, Geoffrey E. Hinton, Tom Michael Mitchell. 2009

1,146 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

In Representation & Self-Supervised Learning

Citations in the corpus

A Neural Network for Factoid Question Answering over Paragraphs (2014, methodology): “While ideally we would have used all answers, our model beneﬁts from many training examples per answer to learn meaningful representations; this issue can possibly be addressed with techniques from zero shot learning (Palatucci et al., 2009; Pasupat and Liang, 2014), which we leave to future work.”
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions (2015, background): “The concept of “Zero-Shot Learning” has been introduced in the literature [7, 8, 16, 21, 5, 17] with the aim to improve the scalability of traditional object recognition systems.”
Zero-Shot Learning Through Cross-Modal Transfer (2013, background): “Unlike previous work on zero-shot learning which can only predict intermediate features or differentiate between various zero-shot classes [21, 27], our joint model can achieve both state of the art accuracy on known classes as well as reasonable performance on unseen classes.”
Learning Deep Representations of Fine-Grained Visual Descriptions (2016, background): “for zero-shot learning [36, 38, 24, 33, 12, 41, 2] and automatically generating image captions for general web images [23, 35, 45, 20, 8].”
Continual Lifelong Learning with Neural Networks: A Review (2018, background): “ticular samples, assuming the simultaneous availability of multiple 17 learning tasks with the aim to improve the performance at one speciﬁc task. Similarly, zero-shot learning (Lampert et al., 2009; Palatucci et al., 2009) and one-shot learning (Fei-Fei et al., 2003; Vinyals et al., 2016) aim at pe”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, methodology): “We have incorporated knowledge into the embeddings of Reddit posts by (i) using Zero Shot learning(Palatucci et al. 2009), (ii) modulating (e.”
Transductive Multi-View Zero-Shot Learning (2015, background): “X S ∈ R n s × t and X T ∈ R n T × t denote the t − dimensional low-level feature vectors of auxiliary and target instances respectively. z S and z T are the auxiliary and target class label vectors.”
Gradient Episodic Memory for Continual Learning (2017, background): “Zero-shot learning [Lampert et al., 2009, Palatucci et al., 2009] and one-shot learning [Fei-Fei et al., 2003, Vinyals et al., 2016, Santoro et al., 2016, Bertinetto et al., 2016] aim at performing well on unseen tasks, but ignore the catastrophic forgetting of previously learned tasks.”
Joint Embedding of Words and Labels for Text Classification (2018, background): “It is particularly successful on the task of zero-shot learning (Palatucci et al., 2009; Yogatama et al., 2015; Ma et al., 2016), where the label correlation captured in the embedding space can improve the prediction when some classes are unseen.”
Deep Metric Learning via Lifted Structured Feature Embedding (2015, background): “Figure 2c illustrates the concept of of transforming a training batch of examples to a fully connected dense matrix of pairwise distances.”
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly (2017, methodology): “Among those, SOC [29] maps the image features into the semantic space and then searches the nearest class embedding vector.”
Zero-Shot Learning by Convex Combination of Semantic Embeddings (2013, background): “Recently, zero-shot learning [10, 14] has received a growing amount of attention [16, 11, 6, 18].”
Zero-Shot Learning — The Good, the Bad and the Ugly (2017, neutral): “compatibility learning frameworks [3, 4, 11, 15, 27, 32, 34, 39, 32, 12, 29, 1, 6, 24, 13, 21].”
An embarrassingly simple approach to zero-shot learning (2015, methodology): “Zero-shot learning (ZSL) is a relatively recent machine learning paradigm that was introduced in the works [21, 28], and quoting the latter, it aims to tackle the following question:”
Evaluation of output embeddings for fine-grained image classification (2014, methodology): “Similarly, [40, 50] use the regression objective and CCA [20] maximizes the correlation of input and output embeddings.”
Open-world Learning and Application to Product Classification (2018, background): “e proposed framework is also related to zero-shot learning [22, 24, 33] (in that we do not require training but need to read training examples), k-nearest neighbors (kNN) (with additional rejection capability, metric learning [36] and learning to vote), and Siamese networks [4, 18, 35] (regarding p”
Latent Embeddings for Zero-Shot Classification (2016, methodology): “Therefore, this framework can be applied to zero-shot learning [1, 2, 26, 32, 35].”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “These attributes can be a set of nameable image characteristics and semantic concepts [84, 130, 158, 88], or latent topics discovered by the system from the data [199, 131, 89].”