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From MetaGAN to MetaGAIL
Feng XuLAMDA5, Nanjing University
December 8, 2020
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Table of Contents
MetaGAN
GANs and GAILs
MetaGAN and MetaGAIL
References
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Table of Contents
MetaGAN
GANs and GAILs
MetaGAN and MetaGAIL
References
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Introduction
MetaGAN: An Adversarial Approach to Few-Shot Learning. [1]Ruixiang Zhang et al. (Yoshua Bengio)Motivation:How to form generalizable decision boundaries from a small number oftranining samples in few shot learning?
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Contribution
• Propose MetaGAN that unify supervised/semi-supervised few-shot learningnaturally
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Formalization
Distribution of Tasks P(T)Training tasks (Ti)N
i=1, where T = (ST,QT)ST = Ss
T ∪ SuT,QT = Qs
T ∪ QuT, S/Q denote support/query set, s/u denote
superversied/unsupervisedSu
T = {x1, x2, ...XM},QsT = {(x1, y1), ...(XO, yO)}
Objective: Minimize prediction loss on a query set given support set
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Trouble of GANs• GAN have trouble generating realistic samples in complex datasets• Easy to run into mode collapsing
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AlgorithmAssume a few-shot classifier pD(x;T) = (p1(x), p2(x), ...pn(x)), augmented with anadditional output pn+1(x) to model the probability that input data is fake. Theobjectives areDiscriminator:
LTD = Lsuperversied + Lunsupervised
Lsupervised = Ex,y∼QsTlogpD(y|x, y ≤ N)
Lunsupervised = Ex∼QuTlogpd(y ≤ N|x) + Ex∼pT
G(N + 1|x)
Generator:LT
G(D) = −Ex∼PTG[log(pD(y ≤ N|x))]
Overall:LD = maxDET∼P(T)LT
D
LG = minGET∼P(T)LTG
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Practical implementation of meta Discriminator
• MAML:θ′d = θd − α∇θd lTDlTD = −Ex,y∼Ss
TlogpD(y|x, y ≤ N)− Ex∼Su
TlogpD(y ≤ N|x)− Ex∼pT
GlogpD(N + 1|x)
• Relation Network [2]: do classification via a deep distance metricLet ri,j = gψ(C(fϕ(xi), fϕ(xj)), xi ∈ Ss
T, xj ∈ QsT be the relevance score between
query xj and support xi, where gϕ is the relation module, fϕ is the embeddingnetwork, C is the concatenation operator. ri,j is computed computed via softmaxclassification
pD(y = k|xj) =exp(rk,j)
1 +∑N
i=1 exp(ri,j)
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Practical implementation of Generator
A conditional generative mode: compress support dataset to a vector hT andconcatenate it with random noise z as input
1. Instance-Encoder Module:xi ∼ Ss
T → ei = Instance − Encoder(xi)
2. Feature-Aggregation Module: element wise operators such as average pooling,max pooling.
To make it harder for the generator to simply reconstricut its inputs
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An insight
(a) MetaGAN
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Performance
(b) Supervised Case
(c) Semi-Supervised Case
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Table of Contents
MetaGAN
GANs and GAILs
MetaGAN and MetaGAIL
References
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GAN/GAIL Framework
(d) GAN framework
(e) GAIL framework
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each GAN’s corresponding GAIL
• GAN [3] & GAIL [4]• CGAN [5] & CGAIL (additional label input)• InfoGAN [6] & InfoGAIL (learnt latent variable)• ACGAN & ACGAIL (augment D with an auxiliary classifier)• f-GAN & f-GAIL (minimize f-divergence of data distribution)• GoalGAN & GOALGAIL (Generate goals)• TripleGAN& TripleGAIL
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Table of Contents
MetaGAN
GANs and GAILs
MetaGAN and MetaGAIL
References
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Table of Contents
MetaGAN
GANs and GAILs
MetaGAN and MetaGAIL
References
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ReferencesRuixiang Zhang et al. “MetaGAN: An Adversarial Approach to Few-ShotLearning”. In: NIPS. 2018, pp. 2371–2380.Flood Sung et al. “Learning to Compare: Relation Network for Few-ShotLearning”. In: CVPR. IEEE Computer Society, 2018, pp. 1199–1208. url:http://openaccess.thecvf.com/content\_cvpr\_2018/html/Sung\_Learning\_to\_Compare\_CVPR\_2018\_paper.html.Ian J. Goodfellow et al. “Generative Adversarial Nets”. In: NIPS. Ed. byZoubin Ghahramani et al. 2014, pp. 2672–2680.Jonathan Ho and Stefano Ermon. “Generative Adversarial Imitation Learning”. In:NIPS. 2016, pp. 4565–4573.Mehdi Mirza and Simon Osindero. “Conditional Generative Adversarial Nets”. In:CoRR abs/1411.1784 (2014).Xi Chen et al. “InfoGAN: Interpretable Representation Learning by InformationMaximizing Generative Adversarial Nets”. In: NIPS. 2016, pp. 2172–2180.
