Introduction to Embedding Space
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Embedding Space sentence examples within convolutional neural network
The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN).
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A multi-stream convolutional neural network (CNN) framework is proposed to effectively learn the 3D volume and 2D MIP feature vectors, respectively, and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the 2D feature vectors into the 3D volume embedding space.
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Embedding Space sentence examples within cross modal retrieval
Then, armed with this dataset, we describe several approaches which leverage scene text, including a better scene-text aware cross-modal retrieval method which uses specialized representations for text from the captions and text from the visual scene, and reconcile them in a common embedding space.
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In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval.
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Embedding Space sentence examples within pre trained word
Specifically, we learn an encoder to generate a debiased version of an input word embedding such that it (a) retains the semantics of the pre-trained word embedding, (b) agrees with the unbiased definition of the word according to the dictionary, and (c) remains orthogonal to the vector space spanned by any biased basis vectors in the pre-trained word embedding space.
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The novel contributions of our solution include: (i)the introduction of a novel data representation for topic modeling based on syntactic and semantic relationships derived from distances calculated within a pre-trained word embedding space and (ii)the proposal of a new TF-IDF-based strategy, particularly developed to weight the CluWords.
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Embedding Space sentence examples within Joint Embedding Space
Our network consists of two components: a generator to synthesize gestures from a joint embedding space of features encoded from the input speech and the seed poses, and a discriminator to distinguish between the synthesized pose sequences and real 3D pose sequences.
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Towards overcoming this challenge, we propose Hierarchical Moment Alignment Network (HMAN) which learns an effective joint embedding space for moments and sentences.
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Embedding Space sentence examples within Dimensional Embedding Space
Embedding Space sentence examples within Word Embedding Space
Our hypothesis is that a word's representations in both word embedding spaces are more similar for non-biased words than biased words.
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We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class.
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Embedding Space sentence examples within Latent Embedding Space
The proposed DLCL module lends on the idea of latent concepts to learn compact representations in the latent embedding space in an unsupervised way.
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Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even logic of original sentences, mainly due to the large unconstrained model space or too simplified assumptions on latent embedding space.
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Embedding Space sentence examples within Common Embedding Space
Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action.
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Then, armed with this dataset, we describe several approaches which leverage scene text, including a better scene-text aware cross-modal retrieval method which uses specialized representations for text from the captions and text from the visual scene, and reconcile them in a common embedding space.
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Embedding Space sentence examples within Semantic Embedding Space
We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons.
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To ensure the model's extendibility, it embeds candidate answers and recognized texts in a semantic embedding space and adopts semantic embedding based classifier to perform answer prediction.
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Embedding Space sentence examples within Learned Embedding Space
Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space.
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It formalizes an end-to-end network architecture, referred to as b-Net, which accomplishes noise suppression through attention masking in a learned embedding space.
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Embedding Space sentence examples within Feature Embedding Space
The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN).
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Among different few-shot learning algorithms, metric-based methods, which focus on how to acquire a robust feature embedding space or distance metric for classification, are most studied and believed to be effective for classification tasks.
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Embedding Space sentence examples within Shared Embedding Space
Visual-semantic embedding (VSE) networks create joint image-text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image-text retrieval, image captioning, and visual question answering.
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More specifically, our work o↵ers (i) an approach to learn neural representations of product attribute-values within a shared embedding space as product reviews; (ii) a weighted composition strategy to develop product representations from the representation of its attributes; and, (iii) a review selection method that selects relevant reviews for the composed product representation within the neural embedding space.
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Embedding Space sentence examples within Unified Embedding Space
Existing methods usually learn a similarity mapping of local parts between image and text, or embed the whole image and text into a unified embedding space.
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The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries.
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Embedding Space sentence examples within Discriminative Embedding Space
To this end, we first learn a discriminative embedding space for vehicle-terrain interaction sounds from triplets of audio clips formed using visual features of the corresponding terrain patches and cluster the resulting embeddings.
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In this work, we propose to seek an angularly discriminative Embedding Space where representations are continuous and have smaller intra-class angular margin and larger inter-class angular margin.
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Embedding Space sentence examples within Specific Embedding Space
Based on these modules, we propose a hierarchical attribute-aware embedding network (HAEN) which takes images and attributes as input, learns multiple attribute-specific embedding spaces, and measures fine-grained similarity in the corresponding spaces.
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Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query.
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Embedding Space sentence examples within Monolingual Embedding Space
These GANs-based methods enable the alignment of two monolingual embedding spaces approximately, but the performance on the embeddings of low-frequency words (LFEs) is still unsatisfactory.
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One mainstream method in cross-lingual word embeddings is to learn a linear mapping between two monolingual embedding spaces using a training dictionary.
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Embedding Space sentence examples within Deep Embedding Space
We present Motion2Vec that learns a deep embedding space by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while being pushed away from randomly sampled images of other segments, and a time contrastive loss is used to preserve the temporal ordering of the images.
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Metric-learning-based methods, which attempt to learn a deep embedding space on extremely large episodes, have been successfully applied to few-shot classification problems.
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Embedding Space sentence examples within Multimodal Embedding Space
Embedding Space sentence examples within Invariant Embedding Space
Specifically, we optimize the differences between the embeddings of a support set and a query set in order to learn a channel-invariant embedding space for utterances.
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We introduce in this letter a dual-stream mutual information distillation network (MIND-Net), whereby the non-identity specific mutual information (MI) characterized by generic face features coexistent on realistic and synthetic LR face images are distilled to render a resolution-invariant embedding space for LRFR.
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Embedding Space sentence examples within Visual Embedding Space
This problem is especially pronounced in data-scarce settings where the data is relatively small (10% of the large scale MSR-VTT) to cover the rather complex audio-visual embedding space.
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By minimizing the cross-entropy loss between a prediction and a given class label, the NN and its visual embedding space are learned to fulfill a given task.
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Embedding Space sentence examples within Cell Embedding Space
In this paper, we propose a deep neural network model which can embed semantic and contextual information about tabular cells in a low-dimensional cell embedding space.
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In this paper, we propose a method to embed the semantic and contextual information about tabular cells in a low dimension cell embedding space.
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Embedding Space sentence examples within Contextual Embedding Space
Embedding Space sentence examples within Spherical Embedding Space
Besides, we use a generative directional appearance module to estimate and dynamically update the foreground/background class probabilities in a spherical embedding space.
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Second, an effective directional appearance model-based statistics is proposed to represent the target and background on a spherical embedding space for VOS.
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Embedding Space sentence examples within Continuou Embedding Space
We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning.
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First, we propose OptiPrompt, a novel and efficient method which directly optimizes in continuous embedding space.
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Embedding Space sentence examples within Speaker Embedding Space
Unlike the conventional back-end augmentation method which adds noises to the raw audios and then extracts augmented embeddings, in this work, we proposed a noise distribution matching (NDM) based algorithm in the speaker embedding space.
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Lastly, we investigate if the learned multi-channel speaker embedding space can be made more discriminative through a contrastive loss-based fine-tuning.
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Embedding Space sentence examples within Sentence Embedding Space
In this work, we propose an alternative approach to achieving video moment retrieval that requires no textual annotations of videos and instead leverages the existing visual concept detectors and a pre-trained image-sentence embedding space.
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We explore augmentation in the sentence embedding space as well as prototypical embedding space.
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Embedding Space sentence examples within Suitable Embedding Space
We propose EmbedNet, which is trained using a triplet loss for learning a suitable embedding space where the embedding of the word image lies closer to the embedding of the corresponding text transcription.
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Moreover, it adds the reconstruction loss to the objective function combining the dimensionality reduction with clustering to find a more suitable embedding space for clustering.
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Embedding Space sentence examples within Learnt Embedding Space
Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localize objects of interest and extract their shape codes that represent the object shape in a learnt embedding space.
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In addition, they ensure the learnt embedding space possesses the property of proximity preservation.
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Embedding Space sentence examples within Large Embedding Space
By making full use of strong-correlation between adjacent groups, GCC can compress not only consecutive several groups whose bits are valued 1 (or 0) but also a single group so that a large embedding space is provided.
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The learned models are able to successfully and robustly identify the underlying modes governing the system, even with a redundantly large embedding space.
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Embedding Space sentence examples within Emotion Embedding Space
Both of them tend to explore the underlying semantic emotion information but with a shared recognition network or with a shared emotion embedding space, respectively.
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Emotion embedding space learned from references is a straight-forward approach for emotion transfer in encoder-decoder structured emotional text to speech (TTS) systems.
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Embedding Space sentence examples within embedding space vium