In recent years, significant advances made in deep neural networks enabled the creation of groundbreaking technologies such as self-driving cars and voice-enabled personal assistants. Almost all successes of deep neural networks are about prediction, whereas the initial breakthroughs came from generative models. Today, although we have very powerful deep generative modeling techniques, these techniques are essentially being used for prediction or for generating known objects (i.e., good quality images of known classes): any generated object that is a priori unknown is considered as a failure mode (Salimans et al., 2016) or as spurious (Bengio et al., 2013b). In other words, when prediction seems to be the only possible objective, novelty is seen as an error that researchers have been trying hard to eliminate. This thesis defends the point of view that, instead of trying to eliminate these novelties, we should study them and the generative potential of deep nets to create useful novelty, especially given the economic and societal importance of creating new objects in contemporary societies. The thesis sets out to study novelty generation in relationship with data-driven knowledge models produced by deep generative neural networks. Our first key contribution is the clarification of the importance of representations and their impact on the kind of novelties that can be generated: a key consequence is that a creative agent might need to rerepresent known objects to access various kinds of novelty. We then demonstrate that traditional objective functions of statistical learning theory, such as maximum likelihood, are not necessarily the best theoretical framework for studying novelty generation. We propose several other alternatives at the conceptual level. A second key result is the confirmation that current models, with traditional objective functions, can indeed generate unknown objects. This also shows that even though objectives like maximum likelihood are designed to eliminate novelty, practical implementations do generate novelty. Through a series of experiments, we study the behavior of these models and the novelty they generate. In particular, we propose a new task setup and metrics for selecting good generative models. Finally, the thesis concludes with a series of experiments clarifying the characteristics of models that can exhibit novelty. Experiments show that sparsity, noise level, and restricting the capacity of the net eliminates novelty and that models that are better at recognizing novelty are also good at generating novelty

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Deep generative neural networks for novelty
generation:
a foundational framework, metrics and experiments
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Mehdi Cherti
LAL/CNRS, Université Paris Saclay
Supervised by:
- Balàzs Kégl (LAL/CNRS, Université Paris Saclay)
- Akın Kazakçı(Mines Paristech)

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Prediction
Quest for artificial intelligence

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Prediction Novelty generation
Quest for artificial intelligence

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How to study novelty generation ?
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Studying novelty generation:
•Design theory
•Computational creativity
•Machine learning

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Design theory
• Early work: (Simon, 1969, 1973), Design as
‘problem solving’ (i.e. moving from an initial state
to a desired state)
• C-K theory: (Hatchuel et al., 2003), Design as
joint expansion of knowledge and concepts
• Various formalisms of knowledge (Set Theory
(Hatchuel et al, 2007), Graphs (Kazakci et al,
2010), Matroids (Le Masson et al, 2017))

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• Through C-K, it acknowledges that
knowledge is central
• But lacks computer-based experimental
tools
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Design theory

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Generation as optimization with
evolutionary algorithms
Computational creativity

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• Enables experimentation but the end-
goal is the object itself rather than
studying the generative process
• Fitness function barrier
• No representation learning
• Generation and evaluation are
disconnected
Computational creativity

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Machine learning proposes powerful
generative models,

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but these powerful models are used
to regenerate objects that we can
relate easily to known objects…

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• Although trained for
generating what we know,
some models can generate
unrecognizable objects
• However, these models and
samples are considered as
spurious (Bengio et al. 2013),
or as a failure(Salimans et al.
2016)
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Instead of ignoring or eliminating novelty,
we should study it.
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• Goal of the thesis: study generative potential of deep generative
networks (DGNs) for novelty generation
• Research questions:
• What novelty can be generated by DGN?
• How to evaluate the generative potential of a DGN?
• What are the general characteristics of DGN that can generate
novelty?
• Method: We use computer based simulations with deep generative
models because
• They offer a rich and powerful set of existing techniques
• They can learn (i.e. representations of objects)
• Their generative potential has not been studied systematically

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Outline
1. Introduction
2.The impact of representations on novelty generation
3. Results
3.a. Studying the generative potential of a deep net
3.b. Evaluating the generative potential of deep nets
3.c. Characteristics of models that can generate novelty
4. Conclusion and perspectives
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2.The impact of representations on
novelty generation

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2.The impact of representations on novelty
generation
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(Reich, 1995)
In design literature, it has been acknowledged that
objects can be represented in multiple ways
What effect do representations have
on novelty generation ?

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• Suppose we have a dataset of 16 letters
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2.The impact of representations on novelty
generation

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• Suppose we represent images in pixel space
• We generate pixels randomly uniformly
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Everything is new,
but no structure
2.The impact of representations on novelty
generation

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• Suppose we re-represent each letter using strokes
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• For instance,
2.The impact of representations on novelty
generation

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2.The impact of representations on novelty
generation

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Pixel space Stroke space
Representations change what you can generate
2.The impact of representations on novelty
generation

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•How do we choose a “useful” representation for
novelty generation ?
•Machine learning, and deep generative models in
particular, provides ways to learn
representations from data
Q: Can we use those learned representations for
generation of novelty even if these models are
not designed to do so ?
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2.The impact of representations on novelty
generation

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2.The impact of representations on novelty
generation
•Noise vs novelty
•Likelihood
•Compression of representations
Summary:

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• What novelty can be generated by deep generative
nets (DGN)?
• How to evaluate the generative potential of a
DGN?
• What are the general characteristics of DGN that
can generate novelty?
Research questions:

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3.Experiments
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• We observed that some models could generate novelty
although not designed to do that
• Thus, deep generative models have an unused generative
potential
• Can we demonstrate this more systematically ?
3.a. Studying the generative potential of a deep net

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3.a. Studying the generative potential of a deep net

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(Kazakci, Cherti, Kégl, 2016)
Train data
Generative
model
Learn
Generate
??
3.a. Studying the generative potential of a deep net

30. We use a convolutional sparse auto-encoder as a
model
Sparsity
Training objective is to
minimize the
reconstruction error
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3.a. Studying the generative potential of a deep net
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31. Reconstruction
Input (dim 625)
Bottleneck
Encode
Decode
Deep autoencoder with a bottleneck from Hinton, G. E., & Salakhutdinov, R. R. (2006).
3.a. Studying the generative potential of a deep net

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• We use an iterative method to generate new images
• Start with a random image
• Force the network to construct (i.e. interpret)
• , until convergence, f(x) = dec(enc(x))
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3.a. Studying the generative potential of a deep net

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3.a. Studying the generative potential of a deep net

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3.a. Studying the generative potential of a deep net
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3.a. Studying the generative potential of a deep net
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36. / 5636Kazakçı, Cherti, Kégl, 2016
3.a. Studying the generative potential of a deep net

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Known Training digits
Representable “Combinations of strokes”
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3.a. Studying the generative potential of a deep net
Our interpretation
of the results:

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Known Training digits
Representable All digits that the model can generate
Valuable All recognizable digits
3.a. Studying the generative potential of a deep net

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Known Training digits
Representable “Combinations of strokes”
Valuable Human selection
3.a. Studying the generative potential of a deep net

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• We have one example of a deep generative
model that can indeed generate novelty
• Can we go further by automatically finding
models that can generate novelty ?
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3.b. Evaluating the generative potential of deep nets

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We designed a new setup and set of
metrics to find models that are capable
of generating novelty
3.b. Evaluating the generative potential of deep nets

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•Training on known classes
•Testing on classes known to the experimenter but
unknown to the model
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Idea: simulate the unknown by
3.b. Evaluating the generative potential of deep nets
Proposed setup: train on digits and test on letters,
where letters are used as a proxy for evaluating
the capacity of models to generate novelty

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Generative
model
Learn
Generate
Q: How many of those are
letters ?
3.b. Evaluating the generative potential of deep nets

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Discriminator
Learn
3.b. Evaluating the generative potential of deep nets
To count letters, we learn a discriminator with
36 classes = 10 for digits + 26 for letters

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Discriminator
Nb. of letters
Predict
3.b. Evaluating the generative potential of deep nets
We then use the discriminator to score the models:

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3.b. Evaluating the generative potential of deep nets
“Nb of letters” score is a proxy for finding
models that generate images that are :
• non trivial
• non recognizable as digits

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• We do a large scale experiment where we train ~1000
models (autoencoders, GANs) by varying their
hyperparameters.
• From each model, we generate 1000 images, then we
evaluate the model using our proposed metrics
• Question we tried to answer:
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Can we find models that can generate novelty ?
3.b. Evaluating the generative potential of deep nets

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• Selecting models for letters count lead to models that
can generate novelty
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• Selecting models for digits count lead to models that
memorize training classes
3.b. Evaluating the generative potential of deep nets

49. Pangrams
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3.b. Evaluating the generative potential of deep nets

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Known Training digits
Representable “Combinations of strokes”
Valuable Letters
3.b. Evaluating the generative potential of deep nets

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We have shown that we can automatically
find models that can generate novelty, as
well as other models that cannot
3.b. Evaluating the generative potential of deep nets

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• Can we characterize the difference between
models that can generate novelty and models
that cannot ?
• We study a particular model architecture
through a series of experiments
3.b. Evaluating the generative potential of deep nets

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• We study the effect of different ways of restricting the
capacity of the representation on the same architecture
• We find that restricting the capacity of the
representation hurts their ability to generate novelty
3.c.Characteristics of
models that can generate novelty
More capacity
Morenovelty

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Conclusion
Main Contributions:
• Importance of representation on novelty generation
• Current models can generate novelty even though not
designed for that
• We propose a new setup and a set of metrics to assess
the capacity of the models to generate novelty
• We show that constraining the capacity of the
representation can be harmful for novelty generation

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Perspectives: immediate next steps
• Explain why existing models can generate novelty
• Propose an explicit training criterion to learn a
representation suitable for novelty generation
• Propose alternatives generative procedures to
random sampling
• Experiment on more complex datasets and
domains

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• Agent evolving over time: dynamic knowledge and
value function
• Multi-agent system so that agents get/give feedback
and cooperate
Perspectives: future

57. Thank you !

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3.a. Studying the generative potential of a deep net
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