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one-shot_learning [2017/11/24 15:03]
one-shot_learning [2019/01/12 11:07] (current)
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 https://​arxiv.org/​pdf/​1711.04043.pdf FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS https://​arxiv.org/​pdf/​1711.04043.pdf FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS
 +We propose to study the problem of few-shot learning with the prism of inference
 +on a partially observed graphical model, constructed from a collection of
 +input images whose label can be either observed or not. By assimilating generic
 +message-passing inference algorithms with their neural-network counterparts,​ we
 +define a graph neural network architecture that generalizes several of the recently
 +proposed few-shot learning models. Besides providing improved numerical performance,​
 +our framework is easily extended to variants of few-shot learning, such
 +as semi-supervised or active learning, demonstrating the ability of graph-based
 +models to operate well on ‘relational’ tasks.
 +https://​arxiv.org/​pdf/​1603.05106.pdf One-Shot Generalization in Deep Generative Models
 +We develop
 +machine learning systems with this important
 +capacity by developing new deep generative
 +models, models that combine the representational
 +power of deep learning with the inferential
 +power of Bayesian reasoning. We develop
 +a class of sequential generative models that
 +are built on the principles of feedback and attention.
 +These two characteristics lead to generative
 +models that are among the state-of-the art
 +in density estimation and image generation. We
 +demonstrate the one-shot generalization ability
 +of our models using three tasks: unconditional
 +sampling, generating new exemplars of a given
 +concept, and generating new exemplars of a family
 +of concepts. ​
 +https://​openreview.net/​forum?​id=r1wEFyWCW Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions ​
 +In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation.
 +https://​arxiv.org/​abs/​1803.00676v1 Meta-Learning for Semi-Supervised Few-Shot Classification
 +To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. ​
 +https://​arxiv.org/​pdf/​1804.00222.pdf Learning Unsupervised Learning Rules
 +t. In this work, we propose instead to directly
 +target a later desired task by meta-learning
 +an unsupervised learning rule, which leads to representations
 +useful for that task. Here, our desired
 +task (meta-objective) is the performance of the
 +representation on semi-supervised classification,​
 +and we meta-learn an algorithm – an unsupervised
 +weight update rule – that produces representations
 +that perform well under this meta-objective. Additionally,​
 +we constrain our unsupervised update
 +rule to a be a biologically-motivated,​ neuron-local
 +function, which enables it to generalize to novel
 +neural network architectures. We show that the
 +meta-learned update rule produces useful features
 +and sometimes outperforms existing unsupervised
 +learning techniques. We show that the metalearned
 +unsupervised update rule generalizes to
 +train networks with different widths, depths, and
 +nonlinearities. It also generalizes to train on data
 +with randomly permuted input dimensions and
 +even generalizes from image datasets to a text
 +https://​arxiv.org/​abs/​1804.07275v1 Deep Triplet Ranking Networks for One-Shot Recognition
 +https://​arxiv.org/​abs/​1512.01192v2 Prototypical Priors: From Improving Classification to Zero-Shot Learning
 +https://​arxiv.org/​abs/​1901.02199v1 FIGR: Few-shot Image Generation with Reptile
 +Our model successfully generates novel images on both MNIST and Omniglot with as little as 4 images from an unseen class. We further contribute FIGR-8, a new dataset for few-shot image generation, which contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8, initial results show that our model can generalize to more advanced concepts (such as "​bird"​ and "​knife"​) from as few as 8 samples from a previously unseen class of images and as little as 10 training steps through those 8 images. ​ https://​github.com/​OctThe16th/​FIGR https://​github.com/​marcdemers/​FIGR-8