Representation and Transfer Learning

Ferenc Huszár

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Representation and Transfer Learning

Week 6: Representation and Transfer Learning

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Ferenc Huszár

Abstract:

In this lecture Ferenc will introduce us to the notions behind representation and transfer learning.

Unsupervised learning

observations \(x_1, x_2, \ldots\)
drawn i.i.d. from some \(p_\mathcal{D}\)
can we learn something from this?

Unsupervised learning goals

can we learn something from this?
- a model of data distribution \(p_\theta(x) \approx p_{\mathcal{D}}(x)\)
  - compression
  - data reconstruction
  - sampling/generation
- a representation \(z=g_\theta(x)\) or \(q_{\theta}(z\vert x)\)
  - downstream classification task
  - data visualisation

UL as distribution modeling

defines goal as modeling \(p_\theta(x)\approx p_\mathcal{D}(x)\)
\(\theta\): parameters
maximum likelihood estimation: \[ \theta^{ML} = \operatorname{argmax}_\theta \sum_{x_i \in \mathcal{D}} \log p_\theta(x_i) \]

Deep learning for modelling distributions

auto-regressive models (e.g. RNNs)
- \(p_{\theta}(x_{1:T}) = \prod_{t=1}^T p_\theta(x_t\vert x_{1:t-1})\)
implicit distributions (e.g. GANs)
- x = \(g_\theta(z), z\sim \mathcal{N}(0, I)\)
flow models (e.g. RealNVP)
- like above but \(g_\theta(z)\) invertible
latent variable models (LVMs, e.g. VAE)
- \(p_\theta(x) = \int p_\theta(x, z) dz\)

Latent variable models

\[ p_\theta(x) = \int p_\theta(x, z) dz \]

Latent variable models

\[ p_\theta(x) = \int p_\theta(x\vert z) p_\theta(z) dz \]

Motivation 1

“it makes sense”

describes data in terms of a generative process
e.g. object properties, locations
learnt \(z\) often interpretable
causal reasoning often needs latent variables

Motivation 2

manifold assumption

high-dimensional data
doesn’t occupy all the space
concentrated along low-dimensional manifold
\(z \approx\) intrinsic coordinates within the manifold

Motivation 3

from simple to complicated

\[ p_\theta(x) = \int p_\theta(x\vert z) p_\theta(z) dz \]

Motivation 3

from simple to complicated

\[ \underbrace{p_\theta(x)}_\text{complicated} = \int \underbrace{p_\theta(x\vert z) }_\text{simple}\underbrace{p_\theta(z)}_\text{simple} dz \]

Motivation 3

from simple to complicated

\[ \underbrace{p_\theta(x)}_\text{complicated} = \int \underbrace{\mathcal{N}\left(x; \mu_\theta(z), \operatorname{diag}(\sigma_\theta(z)) \right)}_\text{simple}\underbrace{\mathcal{N}(z; 0, I)}_\text{simple} dz \]

Motivation 4

variational learning

evaluating \(p_\theta(x)\) is hard
- learning is hard
evaluating \(p_\theta(z\vert x)\) is hard
- inference is hard
variational framework:
- approximate learning
- approximate inference

(Kingma and Welling, 2019) Variational Autoencoder

Variational autoencoder

Decoder: \(p_\theta(x\vert z) = \mathcal{N}(\mu_\theta(z), \sigma_n I)\)
Encoder: \(q_\psi(z\vert x) = \mathcal{N}(\mu_\psi(z), \sigma_\psi(z))\)
Prior: \(p_\theta(z)=\mathcal{N}(0, I)\)

Variational encoder: interpretable \(z\)

Self-supervised learning

basic idea

turn unsupervised problem into supervised one
turn datapoints \(x_i\) into input-output pairs
called auxiliary or pretext task
learn to solve auxiliary task
transfer representation leaned to the downstream task

example: jigsaw puzzles

(Noroozi and Favaro, 2016)

Data-efficiency in downstream task

(Hènaff et al, 2020)

Linearity in downstream task

(Chen et al, 2020)

Several self-supervised methods

auto-encoding
denoising auto-encoding
pseudo-likelihood
instance classification
contrastive learning
masked language models

Example: instance classification

pick random data index \(i\)
randomly transform image \(x_i\): \(T(x_i)\)
auxilliary task: guess data index \(i\) from transformed input \(T(x_i)\)
difficulty: N-way classification

Example: contrastive learning

pick random \(y\)
if \(y=1\) pick two random images \(x_1\), \(x_2\)
if \(y=0\) use same image twice \(x_1=x_2\)
aux task: predict \(y\) from \(f_\theta(T_1(x_1)), f_\theta(T_2(x_2))\)

Example: Masked Language Models

image credit: (Lample and Conneau, 2019)

BERT

Why should any of this work?

Predicting What you Already Know Helps: Provable Self-Supervised Learning

(Lee et al, 2020)

Provable Self-Supervised Learning

Assumptions:

observable \(X\) decomposes into \(X_1, X_2\)
pretext: only given \((X_1, X_2)\) pairs
downstream: we will want to predict \(Y\)
\(X_1 \perp \!\!\! \perp X_2 \vert Y, Z\)
(+1 additional strong assumption)

Provable Self-Supervised Learning

\(X_1 \perp \!\!\! \perp X_2 \vert Y, Z\)

Provable Self-Supervised Learning

\[ X_1 \perp \!\!\! \perp X_2 \vert Y, Z \]

Provable Self-Supervised Learning

\[ 👀 \perp \!\!\! \perp 👄 \vert \text{age}, \text{gender}, \text{ethnicity} \]

Provable Self-Supervised Learning

If \(X_1 \perp \!\!\! \perp X_2 \vert Y\), then

\[ \mathbb{E}[X_2 \vert X_1] = \sum_k \mathbb{E}[X_2\vert Y=k] \mathbb{P}[Y=k\vert X_1 = x_1] \]

Provable Self-Supervised Learning

\[\begin{align} &\mathbb{E}[X_2 \vert X_1=x_1] = \\ &\left[\begin{matrix} \mathbb{E}[X_2\vert Y=1], \ldots, \mathbb{E}[X_2\vert Y=k]\end{matrix}\right] \left[\begin{matrix} \mathbb{P}[Y=1\vert X_1=x_1]\\ \vdots \\ \mathbb{P}[Y=k\vert X_1=x_1]\end{matrix}\right] \end{align}\]

Provable Self-Supervised Learning

\[\begin{align} &\mathbb{E}[X_2 \vert X_1=x_1] = \\ &\underbrace{\left[\begin{matrix} \mathbb{E}[X_2\vert Y=1], \ldots, \mathbb{E}[X_2\vert Y=k]\end{matrix}\right]}_\mathbf{A}\left[\begin{matrix} \mathbb{P}[Y=1\vert X_1=x_1]\\ \vdots \\ \mathbb{P}[Y=k\vert X_1=x_1]\end{matrix}\right] \end{align}\]

Provable Self-Supervised Learning

\[ \mathbb{E}[X_2 \vert X_1=x_1] = \mathbf{A}\left[\begin{matrix} \mathbb{P}[Y=1\vert X_1=x_1]\\ \vdots \\ \mathbb{P}[Y=k\vert X_1=x_1]\end{matrix}\right] \]

Provable Self-Supervised Learning

\[ \mathbf{A}^\dagger \mathbb{E}[X_2 \vert X_1=x_1] = \left[\begin{matrix} \mathbb{P}[Y=1\vert X_1=x_1]\\ \vdots \\ \mathbb{P}[Y=k\vert X_1=x_1]\end{matrix}\right] \]

Provable Self-Supervised Learning

\[ \mathbf{A}^\dagger \underbrace{\mathbb{E}[X_2 \vert X_1=x_1]}_\text{pretext task} = \underbrace{\left[\begin{matrix} \mathbb{P}[Y=1\vert X_1=x_1]\\ \vdots \\ \mathbb{P}[Y=k\vert X_1=x_1]\end{matrix}\right]}_\text{downstream task} \]

Provable self-supervised learning summary

under assumptions of conditional independence
(and that matrix \(\mathbf{A}\) is full rank)
\(\mathbb{P}[Y|x_1]\) is in linear span of \(\mathbb{E}[X_2\vert x_1]\)
All we need is linear model on top of \(\mathbb{E}[X_2\vert x_1]\)
note: \(\mathbb{P}[Y|x_1, x_2]\) would be really optimal

Recap

Variational learning

\[ \theta^\text{ML} = \operatorname{argmax}_\theta \sum_{x_i \in \mathcal{D}} \log p_\theta(x_i) \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \log p_\theta(x_i) - \operatorname{KL}[q_\psi(z\vert x_i) \| p_\theta(z\vert x_i)] \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \log p_\theta(x_i) + \mathbb{E}_{z\sim q_\psi} \log \frac{p_\theta(z\vert x_i)}{q_\psi(z\vert x_i)} \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \mathbb{E}_{z\sim q_\psi} \log \frac{p_\theta(z\vert x_i) p_\theta(x_i)}{q_\psi(z\vert x_i)} \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \mathbb{E}_{z\sim q_\psi} \log \frac{p_\theta(z, x_i)}{q_\psi(z\vert x_i)} \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \mathbb{E}_{z\sim q_\psi(z\vert x_i)} \log p(x_i\vert z) - \operatorname{KL}[q_\psi(z\vert x_i)\vert p_\theta(z)] \]

Variational learning

\[ \mathcal{L}(\theta, \psi) = \sum_{x_i \in \mathcal{D}} \underbrace{\mathbb{E}_{z\sim q_\psi(z\vert x_i)} \log p(x_i\vert z)}_\text{reconstruction} - \operatorname{KL}[q_\psi(z\vert x_i)\vert p_\theta(z)] \]

Discussion of max likelihood

trained so that \(p_\theta(x)\) matches data
evaluated by how useful \(p_\theta(z\vert x)\) is
there is a mismatch

Representation learning vs max likelihood

Discussion of max likelihood

max likelihood may not produce good representations
Why do variational methods find good representations?
Are there alternative principles?