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Title: Integer Networks for Data Compression with Latent-Variable Models

Paper content:
We consider the problem of using variational latent-variable models for data compression.
For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent representation needs to be subjected to entropy coding.
Range coding as an entropy coding technique is optimal, but it can fail catastrophically if the computation of the prior differs even slightly between the sending and the receiving side.
Unfortunately, this is a common scenario when floating point math is used and the sender and receiver operate on different hardware or software platforms, as numerical round-off is often platform dependent.
We propose using integer networks as a universal solution to this problem, and demonstrate that they enable reliable cross-platform encoding and decoding of images using variational models.
The task of information transmission in today's world is largely divided into two separate endeavors: source coding, or the representation of data (such as audio or images) as sequences of bits, and channel coding, representing sequences of bits as analog signals on imperfect, physical channels such as radio waves BID7 .
This decoupling has substantial benefits, as the binary representations of arbitrary data can be seamlessly transmitted over arbitrary physical channels by only changing the underlying channel code, rather than having to design a new code for every possible combination of data source and physical channel.
Hence, the universal representation of any compressed data today is the binary channel, a representation which consists of a variable number of binary symbols, each with probability 1 2 , and no noise (i.e. uncertainty).
As a latent representation, the binary channel unfortunately is a severe restriction compared to the richness of latent representations defined by many variational latent-variable models in the literature (e.g., BID13 BID22 BID18 , and in particular models targeted at data compression BID23 BID0 .
Variational latent-variable models such as VAEs BID13 consist of an encoder model distribution e(y |
x) bringing the data x into a latent representation y, and a decoder model distribution d(x |
y), which represents the data likelihood conditioned on the latents.
Given an encoder e, we observe the marginal distribution of latents m(y
) = E x [e(y | x
)], where the expectation runs over the (unknown) data distribution.
The prior p(y
) is a variational estimate of the marginal BID1 .By
choosing the parametric forms of these distributions and the training objective appropriately, many such models succeed in representing relevant information in the data they are trained for quite compactly (i.e., with a small expected Kullback-Leibler (KL) divergence between the encoder and the prior, E x D KL [e p]), and so may be called compressive in a sense. However
, not all of them can be directly used for practical data compression, as the representation needs to be further converted into binary (entropy encoded). This conversion
is typically performed by range coding, or arithmetic coding BID20 . Range coding is
asymptotically optimal: the length of the binary sequence quickly converges to the expected KL divergence in bits, for reasonably large sequences (such as, for one image). For this to hold
, the following requirements must be satisfied: Figure 1 : The same image, decoded with a model computing the prior using integer arithmetic (left), and the same model using floating point arithmetic (right). The image was decoded
correctly, beginning in the top-left corner, until floating point round-off error caused a small discrepancy between the sender's and the receiver's copy of the prior, at which point the error propagated catastrophically.• The representation must
be discrete-valued, i.e. have a finite number of states, and be noiseless -i.e. the conditional entropy of the encoder must be zero: DISPLAYFORM0 • All scalar elements of the representation y must be brought into a total ordering, and the prior needs to be written using the chain rule of calculus (as a product of conditionals), as the algorithm can only encode or decode one scalar random variable at a time.• Both sides of the binary
channel (i.e. sender and receiver) must be able to evaluate the prior, and they must have identical instances of it.The latter point is crucial, as range coding is extremely sensitive to differences in p between sender and receiver -so sensitive, in fact, that even small perturbations due to floating point round-off error can lead to catastrophic error propagation. Unfortunately, numerical round-off
is highly platform dependent, and in typical data compression applications, sender and receiver may well employ different hardware or software platforms. Round-off error may even be non-deterministic
on one and the same computer. Figure 1 illustrates a decoding failure in a
model which computes p using floating point math, caused by such computational non-determinism in sender vs. receiver. Recently, latent-variable models have been explored
that employ artificial neural networks (ANNs) to compute hierarchical or autoregressive priors BID22 BID18 , including some of the best-performing learned image compression models BID17 BID14 . Because ANNs are typically based on floating point
math, these methods are vulnerable to catastrophic failures when deployed on heterogeneous platforms.To address this problem, and enable use of powerful learned variational models for real-world data compression, we propose to use integer arithmetic in these ANNs, as floating-point arithmetic cannot presently be made deterministic across arbitrary platforms. We formulate a type of quantized neural network we
call integer networks, which are specifically targeted at generative and compression models, and at preventing computational non-determinism in computation of the prior. Because full determinism is a feature of many existing
, widely used image and video compression methods, we also consider using integer networks end to end for computing the representation itself.
There is a large body of recent research considering quantization of ANNs mostly targeted at image recognition applications.
BID6 train classification networks on lower precision multiplication.
BID11 and BID19 perform quantization down to bilevel (i.e., 1-bit integers) at inference time to reduce computation in classification networks.
More recently, BID24 and others have used quantization during training as well as inference, to reduce computation on gradients as well as activations, and BID5 use non-uniform quantization to remove floating point computation, replacing it completely with integer offsets into an integer lookup table.While the quantization of neural networks is not a new topic, the results from the above techniques focus almost exclusively on classification networks.
BID8 , BID9 , and others have demonstrated that these types of networks are particularly robust to capacity reduction.Models used for image compression, like many generative models, are much more sensitive to capacity constraints since they tend to underfit.
As illustrated in and in figure 3 (right), this class of models is much more sensitive to reductions of capacity, both in terms of network size and the expressive power of the activation function.
This may explain why our experiments with post-hoc quantization of network activations have never yielded competitive results for this class of model (not shown).As
illustrated in figure 1 and table 1, small floating point inconsistencies in variational latent-variable models can have disastrous effects when we use range coding to employ the models for data compression across different hardware or software platforms. The
reader may wonder whether there exists other entropy coding algorithms that can convert discrete latent-variable representations into a binary representation, and which do not suffer from a sensitivity to perturbations in the probability model. Unfortunately
, such an algorithm would always produce suboptimal results for the following reason. The source coding
theorem BID21 ) establishes a lower bound on the average length of the resulting bit sequences, which range coding achieves asymptotically (i.e. for long bit sequences). The lower bound is
given by the cross entropy between the marginal and the prior: DISPLAYFORM0 where |b(y)| is the length of the binary representation of y. If an entropy coding
algorithm tolerates error in the values of p(y | θ), this means it must operate under the assumption of identical probability values for a range of values of θ -in other words, discretize the probability values. Since the cross entropy
is minimal only for p(y | θ) = m(y) (for all y), this would impose a new lower bound on |b(y)| given by the cross entropy with the discretized probabilities, which is greater or equal to the cross entropy given above. Thus, the more tolerant
the entropy coding method is to errors in p, the further it deviates from optimal performance. Moreover, it is hard to
establish tolerance intervals for probability values computed with floating point arithmetic, in particular when ANNs are used, due to error propagation. Hence, it is generally
difficult to provide guarantees that a given tolerance will not be exceeded. For similar reasons, current
commercial compression methods model probabilities exclusively in the discrete domain (e.g., using lookup tables; BID16 .Our approach to neural network
quantization is the first we are aware of which specifically addresses non-deterministic computation, as opposed to computational complexity. It enables a variety of possible
variational model architectures and distributions to be effectively used for platformindependent data compression. While we aren't assessing its effects
on computational complexity here, it is conceivable that complexity reductions can also be achieved with the same approach; this is a topic for future work.

assistant: text: We train variational models with quantized networks for computational determinism. This enables using them for cross-platform data compression.