Zero-Shot Learning

165 papers with code • 6 benchmarks • 13 datasets

( Image credit: Prototypical Networks for Few shot Learning in PyTorch )

You can view blog posts such as this to get a high-level understanding:

Greatest papers with code

Sampling Matters in Deep Embedding Learning

CompVis/metric-learning-divide-and-conquer ICCV 2017

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

Clustering Face Verification +3

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

amzn/xfer ICLR 2020

The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.

Few-Shot Image Classification Meta-Learning +3

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

edgarschnfld/CADA-VAE-PyTorch CVPR 2019

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

Few-Shot Learning Generalized Few-Shot Learning +3

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

edgarschnfld/CADA-VAE-PyTorch 5 Dec 2018

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

Few-Shot Learning Generalized Zero-Shot Learning

Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs

nle-ml/mmkb 7 Sep 2017

A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.

Image Retrieval Knowledge Graph Embedding +3

Insights from the Future for Continual Learning

arthurdouillard/incremental_learning.pytorch 24 Jun 2020

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting.

Continual Learning Representation Learning +1

Learning a Deep Embedding Model for Zero-Shot Learning

lzrobots/DeepEmbeddingModel_ZSL CVPR 2017

In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space.

Image Captioning Zero-Shot Learning

75 Languages, 1 Model: Parsing Universal Dependencies Universally

hyperparticle/udify IJCNLP 2019

We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages.

Dependency Parsing Zero-Shot Learning

No Fuss Distance Metric Learning using Proxies

dichotomies/proxy-nca ICCV 2017

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.

Metric Learning Semantic Similarity +2

Zero-Shot Semantic Segmentation

valeoai/ZS3 NeurIPS 2019

Semantic segmentation models are limited in their ability to scale to large numbers of object classes.

General Classification Semantic Segmentation +2