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Title: Exploring the Correlation between Likelihood of Flow-based Generative Models and Image Semantics

Paper content:
Among deep generative models, flow-based models, simply referred as \emph{flow}s in this paper, differ from other models in that they provide tractable likelihood.
Besides being an evaluation metric of synthesized data, flows are supposed to be robust against out-of-distribution~(OoD) inputs since they do not discard any information of the inputs.
However, it has been observed that flows trained on FashionMNIST assign higher likelihoods to OoD samples from MNIST.
This counter-intuitive observation raises the concern about the robustness of flows' likelihood.
In this paper, we explore the correlation between flows' likelihood and image semantics.
We choose two typical flows as the target models: Glow, based on coupling transformations, and pixelCNN, based on autoregressive transformations.
Our experiments reveal surprisingly weak correlation between flows' likelihoods and image semantics: the predictive likelihoods of flows can be heavily affected by trivial transformations that keep the image semantics unchanged, which we call semantic-invariant transformations~(SITs).
We explore three SITs~(all small pixel-level modifications): image pixel translation, random noise perturbation, latent factors zeroing~(limited to flows using multi-scale architecture, e.g. Glow).
These findings, though counter-intuitive, resonate with the fact that the predictive likelihood of a flow is the joint probability of all the image pixels.
So flows' likelihoods, modeling on pixel-level intensities, is not able to indicate the existence likelihood of the high-level image semantics.
We call for attention that it may be \emph{abuse} if we use the predictive likelihoods of flows for OoD samples detection.
Deep generative models have been very successful in image generation (Brock et al., 2018; Kingma & Dhariwal, 2018; Miyato et al., 2018) , natural language generation (Bowman et al., 2015; Yu et al., 2017) , audio synthesis and so on.
Among them, generative adversarial networks (GANs) are implicit generative models (Goodfellow et al., 2014) that explicit likelihood function is not required, and are trained by playing a minimax game between the discriminator and the generator; Variational auto-encoders (VAEs, Kingma & Welling (2013) ; Rezende et al. (2014) ) are latent variable generative models optimized by maximizing a lower bound, called evidence lower bound, of the data log-likelihood.
Flow-based models (Dinh et al., 2016; differ from them in that they provide exact log-likelihood evaluation with change of variables theorem (Rezende & Mohamed, 2015) .
A flow usually starts with a simple base probability distribution, e.g. diagonal Gaussian, then follows a chain of transformations in order to approximate complex distributions.
Each transformation is parameterized by specially designed neural networks so that the log-determinant of its Jacobian can be efficiently computed.
Most of the previous works focus on how to design more flexible transformations to achieve tighter log-likelihoods, and generate more realistic samples.
It is also believed that flows can be used to detect out-of-distribution(OoD) samples by assigning low likelihoods on them.
However, it has been observed that flows fail to do so.
For example, flows trained on FashionMNIST surprisingly assign higher likelihoods on MNIST samples (Nalisnick et al., 2018; Choi & Jang, 2018) .
Though analyses on pixel-level statistics are performed on this phenomenon (Nalisnick et al., 2018) , and density evaluation combined with uncertainty estimation is used to detect OoD samples (Choi & Jang, 2018) , the reasons behind flows' counter-intuitive behaviours are still not clear.
Humans easily discriminate MNIST images from FashionMNIST images, since their high-level image semantics are perceptually different.
Accordingly, it takes some metrics that can reflect the high-level image semantics for OoD detection.
In this paper, we empirically explore the correlation between flows' likelihoods and image semantics, and question the rationality and applicability of using predictive likelihoods of flows for OoD detection.
We first introduce a concept of semanticinvariant transformation (SIT).
An SIT transforms an input without changing its high-level semantics, e.g. a dog image through an SIT is still supposed to be recognized as a dog.
We choose two typical flow-based models as target models: Glow (Kingma & Dhariwal, 2018) , based on coupling transformations, and pixelCNN , based on autoregressive transformations.
We evaluate on image datasets MNIST and FashionMNIST under three trivial SITs: image translation, random noise perturbation, and latent factors zeroing (specific to invertible flows using multi-scale architectures, e.g. Glow).
We demonstrate that the predictive likelihoods of the target models show weak correlation to the image semantics in the following ways:
• Small pixel translations of test images could result in obvious likelihood decreases of Glow.
• Perturbing small random noises, unnoticeable to humans, to test images could lead to catastrophic likelihood decreases of target models.
This also applies even if we keep the semantic object of a test image intact, and only add noises to the background.
• For an invertible flow using multi-scale architecture, e.g. Glow, the inferred latent variables of an image is a list of gaussianized and standardized factors.
We find that the contributions of a flow's blocks to the log-likelihood are constant and independent of inputs.
Thus, simply zeroing the preceding latent factors of a sample image, and feed them to flow's reverse function.
We could obtain new samples with surprisingly higher likelihoods, yet with perceptually unnoticeable changes from the original image.
We emphasize that all these SITs are small pixel-level modifications on test images, and undoubtedly have no influences on humans' recognition of the semantic objects in the images.
However, they lead to obvious inconsistency of flows' likelihoods on test samples.
Considering that the predictive likelihood of a flow is the joint probability of all the image pixels, it may not convincingly indicate the existence of a semantic object in an image.
Thus it could be problematic to use flows for downstream tasks which require metrics that can reflect image semantics, e.g. OoD detection.
What is the problem of likelihood-based generative models?
Discriminative classifiers, trained to extract class-relevant features, are known to be vulnerable to adversarial examples, and give over-confident predictions even for OoD samples.
Generative models are supposed to be more robust since they model every pixel information of an image.
However, likelihood modeling in high-dimensional space can be hard and lead to counter-intuitive observations.
It was observed that likelihood-based generative models can assign even higher likelihoods on OoD samples (Nalisnick et al., 2018; Choi & Jang, 2018) .
Nalisnick et al. (2018) observe this phenomenon on both flows and VAEs.
They decompose the change-of-variable theorem and investigate the influences of different transformation layers, find that the phenomenon still exists regardless of whether the transformation is volume-preserving or not.
Their second-order analysis on pixel statistics suggests that OoD datasets, e.g. MNIST, just sit inside of in-distribution datasets, e.g. FashinMNIST, with roughly the same mean, smaller variance.
They suspect that flows may simply fit the pixel intensities without really capture the high-level semantics.
Ren (2019) find that the likelihood of an image is mostly dominated by the irrelevant background pixels, and propose a remedy to correct the original likelihood with a likelihood ratio.
Though significantly improves the accuracy of OoD detection, but still fail to answer the question: whether the likelihood ratio shows high correlation to high-level semantics.
This paper differs from previous works and step further to explore the correlations between the likelihood of flow-based generative models and image semantics.
Theoretical analyses in (Theis et al., 2015; van den Oord & Dambre, 2015) point out an important argument that generative models' ability to produce plausible samples is neither sufficient nor necessary for high likelihood.
Results in this paper provide more experimental evidences for this simple argument that even for powerful exact likelihood-based generative models-flows, the likelihoods of samples can be largely weakly correlated to the high-level image semantics.
Thus, special attention should be paid to this argument before we apply likelihood-based generative models to downstream tasks.
For example, considering the weak correlation between flows' likelihoods and image semantics, it may be inappropriate to use them for OoD samples detection.
On the other hand, these counter-intuitive behaviours of flows raise our awareness of the gap between the predictive likelihoods of flows and the expectation that these likelihoods can closely relate to the semantics for OoD detection.
What is exactly the likelihood of a image?
We should keep in mind that the predictive likelihood of a flow is the joint probability of all the image pixels.
There is no doubt that flows, trained by maximizing its likelihood, could generate impressive synthesized data.
There seem to be no problem that in terms of image generation, we expect that every single generated pixel in a image is the most likely one (hinging on its contextual pixels).
However, the likelihood is explicitly modeled on pixels, so can be easily influenced by pixel-level modifications.
Images' likelihoods significantly decrease even small noises are added to the pixels of backgrounds.
For downstream tasks that need some "likelihood" to indicate the object in an image is a cat, rather than a car, the pixels of backgrounds are almost irrelevant.
This drive us to think that we may need to model likelihood in some kind of semantic space or with some "perceptual" metrics, rather than on raw pixels.
One promising direction is to define likelihood of an images on its high-level representation, and successful examples are (Lee, 2018; Nilesh A. Ahuja, 2019) .

assistant: text: show experimental evidences about the weak correlation between flows' likelihoods and image semantics.