Browse > Methodology > Zero-Shot Learning

# Zero-Shot Learning Edit

79 papers with code · Methodology

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# Improving zero-shot learning by mitigating the hubness problem

The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels.

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# Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).

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# Synthesized Classifiers for Zero-Shot Learning

Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided.

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# Zero-Shot Learning by Convex Combination of Semantic Embeddings

19 Dec 2013JudyYe/zero-shot-gcn

In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage.

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# Prototypical Networks for Few-shot Learning

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

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# Learning to Compare: Relation Network for Few-Shot Learning

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

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# Learning Deep Representations of Fine-grained Visual Descriptions

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.

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# Sampling Matters in Deep Embedding Learning

In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.

#3 best model for Metric Learning on CUB-200-2011 (using extra training data)

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# No Fuss Distance Metric Learning using Proxies

Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.

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# Rethinking Knowledge Graph Propagation for Zero-Shot Learning

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.

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