Zero-Shot Learning
297 papers with code • 16 benchmarks • 24 datasets
Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.
Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.
Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.
( Image credit: Prototypical Networks for Few shot Learning in PyTorch )
Further readings:
Benchmarks
These leaderboards are used to track progress in Zero-Shot Learning
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Libraries
Use these libraries to find Zero-Shot Learning models and implementations
ImageNet
CUB-200-2011
Oxford 102 Flower
SuperGLUE
AwA
PASCAL Context
SNIPS
AwA2
aPY
COPA
Subtasks
Most implemented papers
Prototypical Networks for Few-shot Learning
oscarknagg/few-shot
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NeurIPS 2017
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.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Learning to Compare: Relation Network for Few-Shot Learning
floodsung/LearningToCompare_FSL
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CVPR 2018
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.
Learning Deep Representations of Fine-grained Visual Descriptions
hanzhanggit/StackGAN-v2
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CVPR 2016
State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i. e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily.
Zero-shot User Intent Detection via Capsule Neural Networks
congyingxia/ZeroShotCapsule
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User intent detection plays a critical role in question-answering and dialog systems.
Sampling Matters in Deep Embedding Learning
CompVis/metric-learning-divide-and-conquer
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ICCV 2017
In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.
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.
Learning a Deep Embedding Model for Zero-Shot Learning
lzrobots/DeepEmbeddingModel_ZSL
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In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space.
Semantic Autoencoder for Zero-Shot Learning
hoseong-kim/sae-pytorch
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CVPR 2017
We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes.
