Original: I MPROVED C ONTRASTIVE D IVERGENCE T RAINING OF E NERGY B ASED M ODELS
Revised: I MPROVED C ONTRASTIVE D IVERGENCE T RAINING OF E NERGY B ASED M ODELS

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: We propose several different techniques to improve contrastive divergence training of energy-based models (EBMs).
Revised: We propose several different techniques to improve contrastive divergence training of energy-based models (EBMs).

Original: We ﬁrst show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important to avoid training instabilities in previous models.
Revised: We ﬁrst show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important to avoid training instabilities in previous models.

Original: We further highlight how data augmentation, multi-scale processing, and reservoir sampling can be used to improve model robustness and generation quality.
Revised: We further highlight how data augmentation, multi-scale processing, and reservoir sampling can be used to improve model robustness and generation quality.

Original: Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection and compositional generation.
Revised: Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection and compositional generation

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Energy-Based models (EBMs) have received an inﬂux of interest recently and have been applied to realistic image generation (Du & Mordatch, 2019),  out of distribution and adversarial robustness (Lee et al., 2018; Du & Mordatch, 2019; Grathwohl et al., 2019), compositional generation (Hinton, 1999; Du et al., 2020a), memory modeling (Bartunov et al., 2019), text generation (Deng et al., 2020),  reinforcement learning (Haarnoja et al., 2017; Du et al., 2019), protein design and folding (Ingraham et al.;
Revised: Energy-Based models (EBMs) have received an inﬂux of interest recently and have been applied to realistic image generation (Han et al., 2019; Du & Mordatch, 2019), 3D shapes synthesis () , out of distribution and adversarial robustness (Lee et al., 2018; Du & Mordatch, 2019; Grathwohl et al., 2019), compositional generation (Hinton, 1999; Du et al., 2020a), memory modeling (Bartunov et al., 2019), text generation (Deng et al., 2020), video generation (), reinforcement learning (Haarnoja et al., 2017; Du et al., 2019), protein design and folding (Ingraham et al.

Original:  Du et al., 2020b) and biologically-plausible training (Scellier & Bengio, 2017).
Revised: ; Du et al., 2020b) and biologically-plausible training (Scellier & Bengio, 2017).

Original: Contrastive divergence is a popular and elegant procedure for training EBMs proposed by (Hinton, 2002) which lowers the energy of the training data and raises the energy of the sampled confabulations generated by the model.
Revised: Contrastive divergence is a popular and elegant procedure for training EBMs proposed by (Hinton, 2002) which lowers the energy of the training data and raises the energy of the sampled confabulations generated by the model.

Original: The model confabulations are generated via an MCMC process (commonly Gibbs sampling or Langevin dynamics), leveraging the extensive body of research on sampling and stochastic optimization.
Revised: The model confabulations are generated via an MCMC process (commonly Gibbs sampling or Langevin dynamics), leveraging the extensive body of research on sampling and stochastic optimization.

Original: The appeal of contrastive divergence is its simplicity and extensibility.
Revised: The appeal of contrastive divergence is its simplicity and extensibility.

Original: It does not require training additional auxiliary networks (Kim & Bengio, 2016;
Revised: It does not require training additional auxiliary networks (Kim & Bengio, 2016;

Original: Dai et al., 2019) (which introduce additional tuning and balancing demands), and can be used to compose models zero-shot.
Revised: Dai et al., 2019) (which introduce additional tuning and balancing demands), and can be used to compose models zero-shot.

Original: Despite these advantages, training EBMs with contrastive divergence has been challenging due to training instabilities.
Revised: Despite these advantages, training EBMs with contrastive divergence has been challenging due to training instabilities.

Original: Ensuring training stability required either combinations of spectral normalization and Langevin dynamics gradient clipping (Du & Mordatch, 2019), parameter tuning (Grathwohl et al., 2019), early stopping of MCMC chains (Nijkamp et al., 2019b), or avoiding the use of modern deep learning components, such as self-attention or layer normalization (Du & Mordatch, 2019).
Revised: Ensuring training stability required either combinations of spectral normalization and Langevin dynamics gradient clipping (Du & Mordatch, 2019), parameter tuning (Grathwohl et al., 2019), early stopping of MCMC chains (Nijkamp et al., 2019b), or avoiding the use of modern deep learning components, such as self-attention or layer normalization (Du & Mordatch, 2019).

Original: These requirements limit modeling power, prevent the compatibility with modern deep learning architectures, and prevent long-running training procedures required for scaling to larger datasets.
Revised: These requirements limit modeling power, prevent the compatibility with modern deep learning architectures, and prevent long-running training procedures required for scaling to larger datasets.

Original: With this work, we aim to maintain the simplicity and advantages of contrastive divergence training, while resolving stability issues and incorporating complementary deep learning advances.
Revised: With this work, we aim to maintain the simplicity and advantages of contrastive divergence training, while resolving stability issues and incorporating complementary deep learning advances.

Original: An often overlooked detail of contrastive divergence formulation is that changes to the energy function change the MCMC samples, which introduces an additional gradient term in the objective

Original: [Figure]
Revised: [Figure]

Original:  function (see Section 2.1 for details).
Revised: An often overlooked detail of contrastive divergence formulation is that changes to the energy function change the MCMC samples, which introduces an additional gradient term in the objective function (see Section 2.1 for details).

Original: This term was claimed to be empirically negligible in the original formulation and is typically ignored (Hinton, 1999; Liu & Wang, 2017) or estimated via highvariance likelihood ratio approaches (Ruiz & Titsias, 2019).
Revised: This term was claimed to be empirically negligible in the original formulation and is typically ignored (Hinton, 2002; Liu & Wang, 2017) or estimated via highvariance likelihood ratio approaches (Ruiz & Titsias, 2019).

Original: We show that this term can be efﬁciently estimated for continuous data via a combination of auto-differentiation and nearest-neighbor entropy estimators.
Revised: We show that this term can be efﬁciently estimated for continuous data via a combination of auto-differentiation and nearest-neighbor entropy estimators.

Original: We also empirically show that this term contributes signiﬁcantly to the overall training gradient and has the effect of stabilizing training.
Revised: We also empirically show that this term contributes signiﬁcantly to the overall training gradient and has the effect of stabilizing training.

Original: It enables inclusion of self-attention blocks into network architectures, removes the need for capacity-limiting spectral normalization, and allows us to train the networks for longer periods.
Revised: It enables inclusion of self-attention blocks into network architectures, removes the need for capacity-limiting spectral normalization, and allows us to train the networks for longer periods.

Original: We do not introduce any new objectives or complexity - our procedure is simply a more complete form of the original formulation.
Revised: We do not introduce any new objectives or complexity - our procedure is simply a more complete form of the original formulation.

Original: We further present techniques to improve mixing and mode exploration of MCMC transitions in contrastive divergence.
Revised: We further present techniques to improve mixing and mode exploration of MCMC transitions in contrastive divergence.

Original: We propose data augmentation as a useful tool to encourage mixing in
Revised: We propose data augmentation as a useful tool to encourage mixing in

Original: MCMC by directly perturbing input images to related images.
Revised: MCMC by directly perturbing input images to related images.

Original: By incorporating data augmentation as semantically meaningful perturbations, we are able to greatly improve mixing and diversity of
Revised: By incorporating data augmentation as semantically meaningful perturbations, we are able to greatly improve mixing and diversity of

Original: MCMC chains.
Revised: MCMC chains.

Original: We further propose to maintain a reservoir sample of past samples, improving the diversity of MCMC chain initialization in contrastive divergence.
Revised: We further propose to maintain a reservoir sample of past samples, improving the diversity of MCMC chain initialization in contrastive divergence.

Original: We also leverage compositionality of EBMs to evaluate an image sample at multiple image resolutions when computing energies.
Revised: We also leverage compositionality of EBMs to evaluate an image sample at multiple image resolutions when computing energies.

Original: Such evaluation and coarse and ﬁne scales leads to samples with greater spatial coherence, but leaves
Revised: Such evaluation and coarse and ﬁne scales leads to samples with greater spatial coherence, but leaves

Original: MCMC generation process unchanged.
Revised: MCMC generation process unchanged.

Original: We note that such hierarchy does not require specialized mechanisms such as progressive reﬁnement (Karras et al., 2017)
Revised: We note that such hierarchy does not require specialized mechanisms such as progressive reﬁnement (Karras et al., 2017)

Original: Our contributions are as follows: ﬁrstly, we show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important in avoiding training instabilities that previously limited applicability and scalability of energy-based models.
Revised: Our contributions are as follows: ﬁrstly, we show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important in avoiding training instabilities that previously limited applicability and scalability of energy-based models.

Original: Secondly, we highlight how data augmentation and multi-scale processing can be used to improve model robustness and generation quality.
Revised: Secondly, we highlight how data augmentation and multi-scale processing can be used to improve model robustness and generation quality.

Original: Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection, and compositional generation.
Revised: Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection, and compositional generation.

Original:  A N I MPROVED C ONTRASTIVE D IVERGENCE F RAMEWORK FOR E NERGY B ASED M ODELS
Revised:  A N I MPROVED C ONTRASTIVE D IVERGENCE F RAMEWORK FOR E NERGY B ASED M ODELS

Original: Energy based models (EBMs) represent the likelihood of a probability distribution for x ∈ R D as p θ ( x ) = [Equation] [Equation]
Revised: Energy based models (EBMs) represent the likelihood of a probability distribution for x ∈ R D as p θ ( x ) = exp( − E θ ( x ))

Original: ( x ) [Equation] Z ( θ 
Revised: Z ( θ ) where the function

Original: [Equation] where the function E θ ( x ) : R D → R , is known as the energy function , and
Revised: E θ ( x ) : R D → R , is known as the energy function , and

Original: Z ( θ ) =
Revised: Z ( θ ) =

Original: (cid:82) x exp − E θ ( x ) is known as the partition function.
Revised: (cid:82) x exp − E θ ( x ) is known as the partition function.

Original: Thus an EBM can be represented by an neural network that takes x as input and outputs a scalar.
Revised: Thus an EBM can be represented by an neural network that takes x as input and outputs a scalar.

Original: Training an EBM through maximum likelihood (ML) is not straightforward, as Z ( θ ) cannot be reliably computed, since this involves integration over the entire input domain of x .
Revised: Training an EBM through maximum likelihood (ML) is not straightforward, as Z ( θ ) cannot be reliably computed, since this involves integration over the entire input domain of x .

Original: However, the gradient of log-likelihood  can be represented as
Revised: However, the gradient of log-likelihood with respect to a data sample x can be represented as

Original: [Equation]
Revised: [Equation]

Original: Note that Equation 1 is still not tractable, as it requires using Markov Chain Monte Carlo (MCMC) to draw samples from the model distribution p θ ( x ) , which often takes exponentially long to mix.
Revised: Note that Equation 1 is still not tractable, as it requires using Markov Chain Monte Carlo (MCMC) to draw samples from the model distribution p θ ( x ) , which often takes exponentially long to mix.

Original: As a practical approximation to the above objective, (Hinton, 1999) proposes  contrastive divergence 
Revised: As a practical approximation to the above objective, (Hinton, 2002) proposes the contrastive divergence objective

Original: [Equation]
Revised: [Equation]

Original: where Π θ represents a MCMC transition kernel for p θ , and Π tθ ( p ( x )) represents t sequential MCMC transitions starting from p ( x ) .
Revised: where Π θ represents a MCMC transition kernel for p θ , and Π tθ ( p ( x )) represents t sequential MCMC transitions starting from p ( x ) .

Original: The above objective can be seen as an improvement operator, where
Revised: The above objective can be seen as an improvement operator, where

Original: KL [Equation] p
Revised: KL ( p ( x ) || p

Original: [Equation]

Original: x ) || p θ ( x )) ≥ KL (Π tθ ( p ( x ))
Revised: θ ( x )) ≥ KL (Π tθ ( p ( x ))

Original: ||
Revised: ||

Original: p θ ( x )) , because Π θ is converging to equilibrium distribution p θ ( x ) .
Revised: p θ ( x )) , because Π θ is converging to equilibrium distribution p θ ( x ) (Lyu, 2011).

Original: Furthermore, the above objective is only zero (at its ﬁxed point), when Π θ does not change the distribution of p ( x ) , which corresponds to p θ ( x )
Revised: Furthermore, the above objective is only zero (at its ﬁxed point), when Π θ does not change the distribution of p ( x ) , which corresponds to p θ ( x )

Original: = p ( x ) .
Revised: = p ( x ) .

Original: 2. A M ISSING T ERM IN C ONTRASTIVE D IVERGENCE
Revised: 2. A M ISSING T ERM IN C ONTRASTIVE D IVERGENCE

Original: When taking the negative gradient of Equation 2, we obtain the expression
Revised: When taking the negative gradient of the contrastive divergence objective (Equation 2), we obtain the expression

Original: [Equation]
Revised: [Equation]

Original: where for brevity, we summarize Π tθ ( p ( x ))
Revised: where for brevity, we summarize Π tθ ( p ( x ))

Original: =
Revised: =

Original: q θ ( x ) .
Revised: q θ ( x ) .

Original: The ﬁrst two terms are identical to those of
Revised: The ﬁrst two terms are identical to those of

Original: Equation 1 and the third gradient term (which we refer to as the KL divergence term) corresponds to minimizing the divergence between q θ ( x ) and p θ ( x ) .
Revised: Equation 1 and the third gradient term (which we refer to as the KL divergence term) corresponds to minimizing the divergence between q θ ( x ) and p θ ( x ) .

Original: In practice, past contrastive divergence approaches have ignored the third gradient term, which was difﬁcult to estimate and claimed to be empirically negligible (Hinton, 1999).
Revised: In practice, past contrastive divergence approaches have ignored the third gradient term, which was difﬁcult to estimate and claimed to be empirically negligible (Hinton, 1999).

Original: This reduced the overall contrastivedivergence loss to
Revised: These gradients correspond to a joint loss expression L Full , consisting of traditional contrastive loss L CD and a new loss expression L KL . Speciﬁcally, we have L Full = L CD + L KL where L CD is

Original: [Equation]
Revised: [Equation]

Original: ignoring the  KL divergence term corresponding to the loss
Revised: and the ignored KL divergence term corresponding to the loss

Original: [Equation]
Revised: [Equation]

Original: Despite being difﬁcult to estimate, we show that L KL is a useful tool for both speeding up and stabilizing training of EBMs.
Revised: Despite being difﬁcult to estimate, we show that L KL is a useful tool for both speeding up and stabilizing training of EBMs.

Original: Figure 2 illustrates the overall effects of both losses.
Revised: Figure 2 illustrates the overall effects of both losses.

Original: Equationencourage the energy function to assign low energy to real samples and high energy for generated samples.
Revised: Equationencourage the energy function to assign low energy to real samples and high energy for generated samples.

Original: However, only optimizing Equation 4 often leads to an adversarial mode where the energy function learns to simply generate an energy landscape that makes sampling difﬁcult.
Revised: However, only optimizing Equation 4 often leads to an adversarial mode where the energy function learns to simply generate an energy landscape that makes sampling difﬁcult.

Original: The
Revised: The

Original: KL divergenceterm counteracts this effect, and encourages sampling to closely approximate the underlying distribution p θ ( x ) , by encouraging samples to be both low energy under the energy function as well as diverse.
Revised: KL divergence term counteracts this effect, and encourages sampling to closely approximate the underlying distribution p θ ( x ) , by encouraging samples to be both low energy under the energy function as well as diverse.

Original: Next, we discuss our approach towards estimating this KL divergence, and show that it signiﬁcantly improves the stability when training EBMs.
Revised: Next, we discuss our approach towards estimating this KL divergence, and show that it signiﬁcantly improves the stability when training EBMs.

Original: 2. E STIMATING THE MISSING GRADIENT TERM
Revised: 2. E STIMATING THE MISSING GRADIENT TERM

Original: Estimating L KL can further be decomposed into two separate objectives, minimizing the energy of samples from q θ ( x ) , which we refer to as L opt  and maximizing the entropy of samples from q θ ( x ) which we refer to as L ent .
Revised: Estimating L KL can further be decomposed into two separate objectives, minimizing the energy of samples from q θ ( x ) , which we refer to as L opt (Equation 6) and maximizing the entropy of samples from q θ ( x ) which we refer to as L ent (Equation 7).

Original: Minimizing Sampler Energy.
Revised: Minimizing Sampler Energy.

Original: To minimize the energy of samples from q θ ( x ) we can directly differentiate through both the energy function and MCMC sampling.
Revised: To minimize the energy of samples from q θ ( x ) we can directly differentiate through both the energy function and MCMC sampling.

Original: We follow recent work in EBMs and utilize Langevin dynamics (Du & Mordatch, 2019; Nijkamp et al., 2019b; Grathwohl et al., 2019) for our MCMC transition kernel, and note that each step of Langevin sampling is fully differentiable with respect to underlying energy function parameters.
Revised: We follow recent work in EBMs and utilize Langevin dynamics (Du & Mordatch, 2019; Nijkamp et al., 2019b; Grathwohl et al., 2019) for our MCMC transition kernel, and note that each step of Langevin sampling is fully differentiable with respect to underlying energy function parameters.

Original: Precisely, gradient of L opt becomes
Revised: Precisely, gradient of L opt becomes

Original: [Equation]
Revised: [Equation]

Original: opt

Original: [Equation]

Original: where x (cid:48)
Revised: where x (cid:48)

Original: i represents the i th step of Langevin sampling.
Revised: i represents the i th step of Langevin sampling.

Original: To reduce to memory overhead of this differentiation procedure, we only differentiate through the last step of Langevin sampling.
Revised: To reduce to memory overhead of this differentiation procedure, we only differentiate through the last step of Langevin sampling (though we show it the appendix that leads to the same effect as differentiation through Langevin sampling).

Original: Entropy Estimation.
Revised: Entropy Estimation.

Original: To maximize the entropy of samples from q θ ( x ) , we use a non-parametricestimate nearest neighbor entropy estimator (Beirlant et al., 1997), which is shown to be mean square consistent (Kozachenko & Leonenko, 1987) with root-n convergence rate (Tsybakov & Van der Meulen, 1996).
Revised: To maximize the entropy of samples from q θ ( x ) , we use a non-parametric nearest neighbor entropy estimator (Beirlant et al., 1997), which is shown to be mean square consistent (Kozachenko & Leonenko, 1987) with root-n convergence rate (Tsybakov & Van der Meulen, 1996).

Original: The entropy H of a distribution p ( x ) can be estimated through a set X = x 1 , x 2 , . . .
Revised: The entropy H of a distribution p ( x ) can be estimated through a set X = x 1 , x 2 , . . .

Original: , x n of n different points sampled from p ( x ) as H ( p θ ( x )) = 1 n (cid:80) ni
Revised: , x n of n different points sampled from p ( x ) as H ( p θ ( x )) = 1 n (cid:80) ni

Original: =1 ln( n ∗ NN ( x i , X ))
Revised: =1 ln( n · NN( x i , X ))

Original: + O (1) where the function NN ( x i , X ) denotes the nearest neighbor distance of x i to any other data point in X .
Revised: + O (1) where the function NN( x i , X ) denotes the nearest neighbor distance of x i to any other data point in X .

Original: Based off the above entropy estimator, we write L ent as
Revised: Based off the above entropy estimator, we write L ent as

Original: [Equation]
Revised: [Equation]

Original: where we measure the nearest neighbor with respect to a set B of 1000 past samples from MCMC chains (see Section 2.5 for more details).
Revised: where we measure the nearest neighbor with respect to a set B of 1000 past samples from MCMC chains (see Section 2.5 for more details).

Original: We utilize L2 distance as the metric for computing nearest neighbors.
Revised: We utilize L2 distance as the metric for computing nearest neighbors.

Original: Alternatively, Stein’s identity may also be used to estimate entropy, but this requires considering all samples, as opposed to the nearest, becoming computationally intractable.
Revised: Alternatively, Stein’s identity may also be used to estimate entropy, but this requires considering all samples, as opposed to the nearest, becoming computationally intractable.

Revised: Our entropy estimator serves a simple, quick to compute estimator of entropy, that prevents sampling from collapsing.

Original: Empirically, we ﬁnd that the combination of the above terms in L KL signiﬁcantly improves both the stability and generation quality of EBMs, improving robustness across different model architectures.
Revised: Empirically, we ﬁnd that the combination of the above terms in L KL signiﬁcantly improves both the stability and generation quality of EBMs, improving robustness across different model architectures.

Original: 2. D ATA A UGMENTATION T RANSITIONS
Revised: 2. D ATA A UGMENTATION T RANSITIONS

Original: Langevin sampling, our MCMC transition kernel, is prone to falling into local probability modes (Neal, 2011).
Revised: Langevin sampling, our MCMC transition kernel, is prone to falling into local probability modes (Neal, 2011).

Original: In the image domain, this manifests with sampling chains always converging to a ﬁxed image (Du & Mordatch, 2019).
Revised: In the image domain, this manifests with sampling chains always converging to a ﬁxed image (Du & Mordatch, 2019).

Original: A core difﬁculty is that distances between two qualitatively similar images can be signiﬁcantly far away from each in input domain, on which sampling is applied.
Revised: A core difﬁculty is that distances between two qualitatively similar images can be signiﬁcantly far away from each in input domain, on which sampling is applied.

Original: While
Revised: While

Original: L KL encourages different sampling chains to cover the model distribution, Langevin dynamics alone is not enough to encourage large jumps in ﬁnite number of steps.
Revised: L KL encourages different sampling chains to cover the model distribution, Langevin dynamics alone is not enough to encourage large jumps in ﬁnite number of steps.

Original: It is further beneﬁcial to have an individual sampling chain have to ability mix between probability modes.
Revised: It is further beneﬁcial to have an individual sampling chain have to ability mix between probability modes.

Original: [Figure]
Revised: [Figure]

Original: To encourage greater exploration between similar inputs in our model, we propose to augment chains of MCMC sampling with periodic data augmentation transitions that encourages movement between “similar” inputs.
Revised: To encourage greater exploration between similar inputs in our model, we propose to augment chains of MCMC sampling with periodic data augmentation transitions that encourages movement between “similar” inputs.

Original: In particular, we utilize a combination of color, horizontal ﬂip, rescaling, and Gaussian blur augmentations.
Revised: In particular, we utilize a combination of color, horizontal ﬂip, rescaling, and Gaussian blur augmentations.

Original: Such combinations of augmentation has recently seen success applied in unsupervised learning (Chen et al., 2020).
Revised: Such combinations of augmentation has recently seen success applied in unsupervised learning (Chen et al., 2020).

Original: Speciﬁcally, during training time, we initialize MCMC sampling from a data augmentation applied to an input sampled from the buffer of past samples.
Revised: Speciﬁcally, during training time, we initialize MCMC sampling from a data augmentation applied to an input sampled from the buffer of past samples.

Original: At test time, during generation, we apply a random augmentation to the input after everysteps of Langevin sampling.
Revised: At test time, during generation, we apply a random augmentation to the input after everysteps of Langevin sampling.

Original: We illustrate this process in the bottom of Figure 2.
Revised: We illustrate this process in the bottom of Figure 2.

Revised: Data augmentation transitions are always taken.

Original: 2. C OMPOSITIONAL M ULTI - SCALE G ENERATION
Revised: 2. C OMPOSITIONAL M ULTI - SCALE G ENERATION

Original: To encourage energy functions to focus on features in both low and high resolutions, we deﬁne our energy function as the composition (sum) of a set of energy functions operating on different scales of an image, illustrated in Figure 3.
Revised: To encourage energy functions to focus on features in both low and high resolutions, we deﬁne our energy function as the composition (sum) of a set of energy functions operating on different scales of an image, illustrated in Figure 3.

Original: Since the downsampling operation is fully differentiable, Langevin based sampling can be directly applied to the energy function.
Revised: Since the downsampling operation is fully differentiable, Langevin based sampling can be directly applied to the energy function.

Original: In our experiments, we utilize full, half, and quarter resolution image as input and show in the appendix that this improves the generation performance.
Revised: In our experiments, we utilize full, half, and quarter resolution image as input and show in the appendix that this improves the generation performance.

Original: 2. R ESERVOIR S AMPLING
Revised: 2. R ESERVOIR S AMPLING

Original: To encourage q θ ( x ) to match p θ ( x ) , MCMC steps in q θ ( x ) are often initialized from past samples from q θ ( x ) to enable more diverse mode exploration, a training objective known as persistent contrastive divergence (Tieleman, 2008). 
Revised: To encourage q θ ( x ) to match p θ ( x ) , MCMC steps in q θ ( x ) are often initialized from past samples from q θ ( x ) to enable more diverse mode exploration, a training objective known as persistent contrastive divergence (Tieleman, 2008). Du & Mordatch (2019) propose to implement sampling [Table] [Table]

Original: Du & Mordatch (2019) propose to implement sampling from past samples by utilizing a replay buffer of samples from q θ ( x ) interspersed with samples initialized from random noise.
Revised: [Table] [Table] [Figure] from past samples by utilizing a replay buffer of samples from q θ ( x ) interspersed with samples initialized from random noise.

Original: By storing a large batch of past samples, the replay buffer is able to enforce diversity across chains.
Revised: By storing a large batch of past samples, the replay buffer is able to enforce diversity across chains.

Original: However, as samples are initialized from the replay buffer and added to the buffer again, the replay buffer becomes ﬁlled with a set of correlated samples from q θ ( x ) over [Table] [Table] [Table]
Revised: However, as samples are initialized from the replay buffer and added to the buffer again, the replay buffer becomes ﬁlled with a set of correlated samples from q θ ( x ) over time.

Original: [Table]

Original: [Figure] [Figure] time. To encourage a buffer distribution representative of all past samples, we instead use reservoir sampling technique over all past samples from q θ ( x ) .
Revised: To encourage a buffer distribution representative of all past samples, we instead use reservoir sampling technique over all past samples from q θ ( x ) .

Original: This technique has previously been found helpful in balancing replay in reinforcement learning (Young et al., 2018; Isele & Cosgun, 2018;
Revised: This technique has previously been found helpful in balancing replay in reinforcement learning (Young et al., 2018; Isele & Cosgun, 2018;

Original: Rolnick et al., 2019).
Revised: Rolnick et al., 2019).

Original: Under a reservoir sampling implementation, any sample from q θ ( x ) has an equal probability of being the reservoir buffer.
Revised: Under a reservoir sampling implementation, any sample from q θ ( x ) has an equal probability of being the reservoir buffer.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: We perform empirical experiments to validate the following set of questions: (1) What are the effects of each proposed component towards training EBMs?
Revised: We perform empirical experiments to validate the following set of questions: (1) What are the effects of each proposed component towards training EBMs?

Original: (2) Are our trained EBMs able to perform well on downstream applications of EBMs (generation, compositionality, out-of-distribution detection)?
Revised: (2) Are our trained EBMs able to perform well on downstream applications of EBMs (generation, compositionality, out-of-distribution detection)?

Revised: We provide ablations of each of our proposed components in the appendix.

Original: 3. E XPERIMENTAL S ETUP
Revised: 3. E XPERIMENTAL S ETUP

Original: We investigate the efﬁcacy of our proposed approach.
Revised: We investigate the efﬁcacy of our proposed approach.

Original: Models are trained using the Adam Optimizer (Kingma & Ba, 2015), on a single 32GB Volta GPU for CIFAR-10 for 1 day, and for 3 days on32GB Volta GPUs for CelebaHQ, and LSUN datasets.
Revised: Models are trained using the Adam Optimizer (Kingma & Ba, 2015), on a single 32GB Volta GPU for CIFAR-10 for 1 day, and for 3 days on32GB Volta GPUs for CelebaHQ, and LSUN datasets.

Original: We provide detailed training conﬁguration details in the appendix. 
Revised: We provide detailed training conﬁguration details in the appendix. [Figure]

Original: Our improvements are largely built on top of the EBMs training framework proposed in (Du &
Revised: Our improvements are largely built on top of the EBMs training framework proposed in (Du &

Original: Mordatch, 2019).
Revised: Mordatch, 2019).

Original: We use default parameters including a buffer size of 10000, a random reset percentage of 5% and add L2 regularization to output energies, with a coefﬁcient of 10.
Revised: We use default parameters including a buffer size of 10000, a random reset percentage of 5% and add L2 regularization to output energies, with a coefﬁcient of 10.

Original: At the same time, we found that our approach was signiﬁcantly more stable than IGEBM, allowing us to remove aspects of regularization in (Du & Mordatch, 2019).
Revised: At the same time, we found that our approach was signiﬁcantly more stable than IGEBM, allowing us to remove aspects of regularization in (Du & Mordatch, 2019).

Original: We are able to remove the clipping of gradients in Langevin sampling as well as spectral normalization on the weights of the network.
Revised: We are able to remove the clipping of gradients in Langevin sampling as well as spectral normalization on the weights of the network.

Original: In addition,[Figure] we add self-attention blocks and group normalization(Wu & He, 2018) blocks in residual networks of our trained models.
Revised: In addition, we add self-attention blocks and layer normalization blocks in residual networks of our trained models.

Original: In multi-scale architectures, we utilize 3 different resolutions of an image, the original image resolution, half the image resolution and a quarter the image resolution.
Revised: In multi-scale architectures, we utilize 3 different resolutions of an image, the original image resolution, half the image resolution and a quarter the image resolution.

Original: We report detailed architectures in the appendix.
Revised: We report detailed architectures in the appendix.

Original: When evaluating models, we utilize the EMA model with
Revised: When evaluating models, we utilize the EMA model with

Original: EMA weight of 0.999.
Revised: EMA weight of 0.999.

Original: 3. I MAGE G ENERATION
Revised: 3. I MAGE G ENERATION

Original: We evaluate our approach on CIFAR-10, LSUN bedroom ( ? ), and CelebA-HQ (Karras et al., 2017) datasets and analyze our characteristics of our proposed framework.
Revised: We evaluate our approach on CIFAR-10, LSUN bedroom (Yu et al., 2015), and CelebA-HQ (Karras et al., 2017) datasets and analyze our characteristics of our proposed framework.

Original: Additional analysis and ablations can be found in the appendix of the paper.
Revised: Additional analysis and ablations can be found in the appendix of the paper.

Original: Image Quality.
Revised: Image Quality.

Original: We evaluate our approach on unconditional generation in Table 1.
Revised: We evaluate our approach on unconditional generation in Table 1.

Original: On CIFARwe ﬁnd that approach, while not being state-of-the-art, signiﬁcantly outperforms past energy based approaches  (with approximately the same number of parameters), and has performance in the range of recent GAN models on high resolution images.
Revised: On CIFAR-10 we ﬁnd that approach, while not being state-of-the-art, signiﬁcantly outperforms past EBM approaches that based off implicit sampling from an energy landscape (with approximately the same number of parameters), and has performance in the range of recent GAN models on high resolution images.

Original: We further present example qualitative images from CelebA-HQ in Figure 10b and present qualitative images on other datasets in the appendix of the paper.
Revised: We further present example qualitative images from CelebA-HQ in Figure 11b and present qualitative images on other datasets in the appendix of the paper.

Original: We note that our reported SNGAN performance on CelebA-HQ and LSUN Bedroom use default hyperparameters from ImageNet models.
Revised: We note that our reported SNGAN performance on CelebA-HQ and LSUN Bedroom use default hyperparameters from ImageNet models.

Original: Gaps in performance with our model are likely smaller with better dataset speciﬁc hyper-parameters. 
Revised: Gaps in performance with our model are likely smaller with better dataset speciﬁc hyper-parameters. [Figure]

Original: Effect of Data Augmentation.
Revised: Effect of Data Augmentation.

Original: We evaluate the effect of data augmentation on sampling in EBMs.
Revised: We evaluate the effect of data augmentation on sampling in EBMs.

Original: In Figure 5 we show that by combining Langevin sampling with data augmentation transitions, we are able to enable chains to mix across different images, whereas prior works have shown Langevin converging to ﬁxed images.
Revised: In Figure 5 we show that by combining Langevin sampling with data augmentation transitions, we are able to enable chains to mix across different images, whereas prior works have shown Langevin converging to ﬁxed images.

Original: In Figure 6 we further show that given a ﬁx random noise initialization, data augmentation transitions enable to reach a diverse number of different samples, while sampling without data augmentation transitions leads all chains to converge to the same face.
Revised: In Figure 6 we further show that given a ﬁx random noise initialization, data augmentation transitions enable to reach a diverse number of different samples, while sampling without data augmentation transitions leads all chains to converge to the same face.

Original: Mode Convergence.
Revised: Mode Convergence.

Original: We further investigate high likelihood modes of our model.
Revised: We further investigate high likelihood modes of our model.

Original: In Figure 7, we compare very low energy samples (obtained after running gradient descent 1000 steps on an energy function) for both our model with data augmentation and KL loss and a model without either term.
Revised: In Figure 8, we compare very low energy samples (obtained after running gradient descent 1000 steps on an energy function) for both our model with data augmentation and KL loss and a model without either term.

Original: Due to improved mode exploration, we ﬁnd that long temperature samples under our model with data augmentation/KL loss reﬂect typical high likelihood ”modes” in the training dataset, while our baseline models converges tosaturation artifacts and odd textures, also noted in (Nijkamp et al., 2019a).
Revised: Due to improved mode exploration, we ﬁnd that low temperature samples under our model with data augmentation/KL loss reﬂect typical high likelihood ”modes” in the training dataset, while our baseline models converges to odd shapes, also noted in (Nijkamp et al., 2019a).

Original: Stability.
Revised: In Figure 7, we quantitatively measure Inception scores as we run steps of gradient descent with or without data [Figure] augmentation and KL loss. Our Inception score decreases much more slowly, with some degree of degradation expected since low temperature samples have less diversity.

Original:  EBMs are known to difﬁcult to train, and to be sensitive to both the exact architecture and to various hyper-parameters.
Revised: Stability/KL Loss EBMs are known to difﬁcult to train, and to be sensitive to both the exact architecture and to various hyper-parameters.

Original: We found that the addition of a KL term into our training objective signiﬁcantly improved the stability of EBM training, by encouraging the sampling distribution to match the model distribution.
Revised: We found that the addition of a KL term into our training objective signiﬁcantly improved the stability of EBM training, by encouraging the sampling distribution to match the model distribution.

Original: In Figure8, we measure the energy difference between real and generated images over the course training when adding normalization and self-attention layers to models.
Revised: In Figure we measure the energy difference between real and generated images over the course training when adding normalization and self-attention layers to models.

Original: We ﬁnd that with L kl , the energy difference between both is kept at 0, which indicates stable training.
Revised: We ﬁnd that with L kl , the energy difference between both is kept at 0, which indicates stable training.

Original: in contrast, without L kl all models, with the exception of a model with spectral normalization, diverge to a negative energy different of − 1 , indicating training collapse.
Revised: in contrast, without L kl all models, with the exception of a model with spectral normalization, diverge to a negative energy different of − 1 , indicating training collapse.

Revised: Furthermore, the use of spectral normalization by itself, albeit stable, precludes the addition of other modern network components such as layer normalization.

Revised: The addition of the KL term itself is not too expensive, simply requiring an additional nearest neighbor computation during training, which can be relatively insigniﬁcant cost compared to the number of negative sampling steps used during training.

Revised: With a intermediate number of negative sampling steps(60 steps) during training, adding the

Revised: KL term roughly twice as slow as normal training.

Revised: This difference is decreased with larger number of sampling steps.

Original: See appendix for additional analysis of gradient magnitudes of L kl and L cd . 
Revised: Please see the appendix for additional analysis of gradient magnitudes of L kl and L cd . Ablations.

Revised: We ablate each portion of our proposed approach in Table 2.

Revised: We ﬁnd that the KL loss is crucial to the stability of training an EBM

Original: [Figure]
Revised: [Table]

Revised: and ﬁnd that additions such as a multi-scale architecture are not stable without the presence of a KL loss

Original:  [Figure] 
Revised: [Table] [Table] [Figure] [Table]

Original: 3. C OMPOSITIONALITY
Revised: 3. C OMPOSITIONALITY

Original: Energy Based Models (EBMs) have the ability to compose with other models at generation time (Hinton, 1999; Haarnoja et al., 2017; Du et al., 2020a).
Revised: Energy Based Models (EBMs) have the ability to compose with other models at generation time (Hinton, 1999; Haarnoja et al., 2017; Du et al., 2020a).

Original: We investigate to what extent EBMs trained under our new proposed framework can also exhibit compositionality.
Revised: We investigate to what extent EBMs trained under our new proposed framework can also exhibit compositionality.

Original: See (Du et al., 2020a) for a discussion of various compositional operators and applications in EBMs.
Revised: See (Du et al., 2020a) for a discussion of various compositional operators and applications in EBMs.

Original: In particular, we train independent EBMs E ( x | c 1 ) , E ( x | c 2 ) , E ( x | c 3 ) , that learn conditional generative distribution of concept factors c such as facial expression or object position.
Revised: In particular, we train independent EBMs E ( x | c 1 ) , E ( x | c 2 ) , E ( x | c 3 ) , that learn conditional generative distribution of concept factors c such as facial expression or object position.

Original: We test to see if we can compose independent energy functions together to generate images with each concept factor simultaneously.
Revised: We test to see if we can compose independent energy functions together to generate images with each concept factor simultaneously.

Original: We consider compositions the CelebA-HQ dataset, where we train independent energy functions of face attributes of age, gender, smiling, and wavy hair and a high resolution rendered of different objects rendered at different locations, where we train an energy function on size, position, rotation, and identity of the object.
Revised: We consider compositions the CelebA-HQ dataset, where we train independent energy functions of face attributes of age, gender, smiling, and wavy hair and a high resolution rendered of different objects rendered at different locations, where we train an energy function on size, position, rotation, and identity of the object.

Original: Qualitative Results.
Revised: Qualitative Results.

Original: We present qualitative results of compositions of energy functions in Figure 9.
Revised: We present qualitative results of compositions of energy functions in Figure 10.

Original: In both composition settings, our approach is able to successfully generate images with each of conditioned factors, while also being globally coherent.
Revised: In both composition settings, our approach is able to successfully generate images with each of conditioned factors, while also being globally coherent.

Original: The left image shows that as we condition on factors of young, female, smiling, and wavy hair, images generation begins exhibiting each required [Table]
Revised: The left image shows that as we condition on factors of young, female, smiling, and wavy hair, images generation begins exhibiting each required feature.

Original: feature. The right image similarly shows that as we condition on factors of size, type, position, and rotation, image generations begin to exhibit each conditioned attribute.
Revised: The right image similarly shows that as we condition on factors of size, type, position, and rotation, image generations begin to exhibit each conditioned attribute.

Original: We note that ﬁgures are visually consistent in terms of lighting, shadows and reﬂections.
Revised: We note that ﬁgures are visually consistent in terms of lighting, shadows and reﬂections.

Original: We note that generations of thee combination of different factors are only speciﬁed at generation time, with models being trained independently .
Revised: We note that generations of thee combination of different factors are only speciﬁed at generation time, with models being trained independently .

Original: Our results indicate that our framework from training EBMs is a promising direction for high resolution compositional visual generation.
Revised: Our results indicate that our framework for training EBMs is a promising direction for high resolution compositional visual generation.

Revised: We further provide visualization of best comparative compositional model from (Vedantam et al., 2018) in the appendix and ﬁnd that our approach signiﬁcantly outperforms it.

Original: 3. O UT OF D ISTRIBUTION R OBUSTNESS
Revised: 3. O UT OF D ISTRIBUTION R OBUSTNESS

Original: Energy Based Models (EBMs) have also been shown to exhibit robustness to both out-of-distribution and adversarial samples (Du & Mordatch, 2019; Grathwohl et al., 2019).
Revised: Energy Based Models (EBMs) have also been shown to exhibit robustness to both out-of-distribution and adversarial samples (Du & Mordatch, 2019; Grathwohl et al., 2019).

Original: We evaluate out-ofdistribution detection of our trained energy through log-likelihood using the evaluation metrics proposed in Hendrycks & Gimpel (2016).
Revised: We evaluate out-ofdistribution detection of our trained energy through log-likelihood using the evaluation metrics proposed in Hendrycks & Gimpel (2016).

Original: We similarly evaluate out-of-distribution detection of an unconditional CIFAR-10 model.
Revised: We similarly evaluate out-of-distribution detection of an unconditional CIFAR-10 model.

Original: Results.
Revised: Results.

Original: We present out-of-distribution results in Table 2, comparing with both other likelihood models and EBMs and using log-likelihood to detect outliers.
Revised: We present out-of-distribution results in Table 3, comparing with both other likelihood models and EBMs and using log-likelihood to detect outliers.

Original: We ﬁnd that on the datasets we evaluate, our approach signiﬁcantly outperforms other baselines, with the exception of CIFARinterpolations.
Revised: We ﬁnd that on the datasets we evaluate, our approach signiﬁcantly outperforms other baselines, with the exception of CIFARinterpolations.

Original: We note the JEM (Grathwohl et al., 2019) further requires supervised labels to train the energy function, which has to shown to improve out-of-distribution performance.
Revised: We note the JEM (Grathwohl et al., 2019) further requires supervised labels to train the energy function, which has to shown to improve out-of-distribution performance.

Original: We posit that by more efﬁciently exploring modes of the energy distribution at training time, we are able to reduce the spurious modes of the energy function and thus improve out-of-distribution performance.
Revised: We posit that by more efﬁciently exploring modes of the energy distribution at training time, we are able to reduce the spurious modes of the energy function and thus improve out-of-distribution performance.

Original:  R ELATED WORK
Revised:  R ELATED WORK

Original: Our work is related to a large body of work on different approaches for training EBMs.
Revised: Our work is related to a large, growing body of work on different approaches for training EBMs.

Original: Our approach is based onthe contrastive divergence (Hinton, 1999), where an energy function is learned to contrast  samples drawn from a model distribution and the real data.
Revised: Our approach is based on contrastive divergence (Hinton, 2002), where a energy function is trained to contrast negative samples drawn from a model distribution and from real data.

Original: Other approaches  towards  training 
Revised: In recent years, such approaches has been applied to the image domain (Xie et al., 2016; Gao et al., 2018; Du & Mordatch, 2019; Nijkamp et al., 2019b; Grathwohl et al., 2019). (Gao et al., 2018) also proposes a multi-scale approach towards generating images for EBM, but different from our work, uses each sub-scale EBM to initialize the generation of the next EBM. Our works builds on existing works towards contrastive divergence based training of EBMs, and presents improvements in generation and stability. A difﬁculty with contrastive divergence training is the difﬁculty of negative sample generation.

Revised: To sidestep this issue, a separate line of work utilizes an auxiliary network to amortize the negative portions of the sampling procedure (Kim & Bengio, 2016;

Original: EBMs include score matching, where the gradients of an energy function are trained to match the gradients of real data (Hyv ¨ arinen, 2005; Song & Ermon, 2019), and denoising (Sohl-Dickstein et al., 2015; Saremi et al., 2018;
Revised: Kumar et al., 2019; Han et al., 2019; Xie et al., 2018; Song & Ou, 2018). One line of work (Kim & Bengio, 2016; Kumar et al., 2019; Song & Ou, 2018), utilizes a separate generator network for negative image sample generations. In contrast, (Xie et al., 2018), utilize a generator to warm start generations for negative samples and (Han et al., 2019) minimizes a divergence triangle between three models.

Revised: While such approaches enable better qualitative generation, they also lose some of the ﬂexibility of the EBM formulation.

Original: Ho et al., 2020), Finally, some approaches utilize pre-trained networks to approximate portions of  energy training (Kim & Bengio, 2016; Kumar et al., 2019)
Revised: For example, separate energy models can no longer be composed together for generation. In addition, other approaches towards training EBMs seek instead investigate other separate objectives to train the EBM. One such approach is score matching, where the gradients of an energy function are trained to match the gradients of real data (Hyv ¨ arinen, 2005; Song & Ermon, 2019), with a related denoising (Sohl-Dickstein et al., 2015; Saremi et al., 2018; Ho et al., 2020) approach.

Revised: Other approaches in include noise contrastive estimation (Gao et al., 2020) and utilizing a learn Steins discrepancy (Grathwohl et al., 2020).

Original: Most prior work in contrastive divergence has ignored the KL term (Hinton, 1999; Salakhutdinov &
Revised: Most prior work in contrastive divergence has ignored the KL term (Hinton, 1999; Salakhutdinov &

Original: Hinton, 2009).
Revised: Hinton, 2009).

Original: A notable exception is (Ruiz & Titsias, 2019), which obtains a similar KL divergence term to ours.
Revised: A notable exception is (Ruiz & Titsias, 2019), which obtains a similar KL divergence term to ours.

Original: Ruiz & Titsias (2019) use a high variance REINFORCE estimator to estimate the gradient of the KL term, while our approach relies on auto-differentiation and nearest neighbor entropy estimators.
Revised: Ruiz & Titsias (2019) use a high variance REINFORCE estimator to estimate the gradient of the KL term, while our approach relies on auto-differentiation and nearest neighbor entropy estimators.

Original: Differentiation through model generation procedures has previously been explored in other models (Finn & Levine, 2017;
Revised: Differentiation through model generation procedures has previously been explored in other models (Finn & Levine, 2017;

Original: Metz et al., 2016).
Revised: Metz et al., 2016).

Original: Other related entropy estimators include those based on Stein’s identity (Liu et al., 2017) and MINE (Belghazi et al., 2018).
Revised: Other related entropy estimators include those based on Stein’s identity (Liu et al., 2017) and MINE (Belghazi et al., 2018).

Original: In contrast to these approaches, our entropy estimator relies only on nearest neighbor calculation, and does not require the training of an independent neural network.
Revised: In contrast to these approaches, our entropy estimator relies only on nearest neighbor calculation, and does not require the training of an independent neural network.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We propose a simple and general framework for improving generation and ease of training with energy based models.
Revised: We propose a simple and general framework for improving generation and ease of training with energy based models.

Original: We show that the framework enables high resolution compositional image generation and out-of-distribution robustness.
Revised: We show that the framework enables high resolution compositional image generation and out-of-distribution robustness.

Original: In the future, we are interested in further computational scaling of our framework, and applications to domains such as text and reasoning.
Revised: In the future, we are interested in further computational scaling of our framework, and applications to domains such as text and reasoning.

Original: A A PPENDIX A. M ODEL A RCHITECTURES
Revised: M ODEL A RCHITECTURES

Original: We list model architectures used in our experiments in Figure 10.
Revised: We list model architectures used in our experiments in Figure 11.

Original: When training multi-scale energy functions, our ﬁnal output energy function is the sum of energy functions applied to the full resolution image, half resolution image, and quarter resolution image.
Revised: When training multi-scale energy functions, our ﬁnal output energy function is the sum of energy functions applied to the full resolution image, half resolution image, and quarter resolution image.

Original: We use the architecture reported in
Revised: We use the architecture reported in

Original: Figure 10 for the full resolution image.
Revised: Figure 11 for the full resolution image.

Original: The half-resolution models shares the architecture listed in
Revised: The half-resolution models shares the architecture listed in

Original: Figure 10, but with all layers before and including the ﬁrst down-sampled residual block removed.
Revised: Figure 11, but with all layers before and including the ﬁrst down-sampled residual block removed.

Original: Similarily, the quarter resolution models share the architectures listed, but with all layers before two down-sampled residual blocks removed.
Revised: Similarily, the quarter resolution models share the architectures listed, but with all layers before two down-sampled residual blocks removed.

Original: 3x3 conv2d,
Revised: 3x3 conv2d,

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: Self Attention
Revised: Self Attention

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: Global Mean Pooling
Revised: Global Mean Pooling

Original: Dense →
Revised: Dense →(a)

Original: [Figure]
Revised: The model architecture used for CIFAR- ex- periments.

Original: 3x3 conv2d,
Revised: 3x3 conv2d,

Original: ResBlock Down
Revised: ResBlock Down

Original: ResBlock Down ResBlock Down
Revised: ResBlock Down ResBlock Down

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: Self Attention
Revised: Self Attention

Original: ResBlock
Revised: ResBlock

Original: ResBlock Down
Revised: ResBlock Down

Original: Global mean Pooling
Revised: Global mean Pooling

Original: Dense 
Revised: Dense →

Original: [Figure]
Revised: [Figure]

Original: A. E XPERIMENT C ONFIGURATIONS F OR D IFFERENT D ATASETS CIFAR- 
Revised: A. E XPERIMENT C ONFIGURATIONS F OR D IFFERENT D ATASETS CIFAR-

Original: For CIFAR-10, we use 40 steps of Langevin sampling to generate a negative sample.
Revised: For CIFAR-10, we use 40 steps of Langevin sampling to generate a negative sample.

Original: The Langevin sampling step size is set to be 100, with Gaussian noise of magnitude 0.001 at each iteration.
Revised: The Langevin sampling step size is set to be 100, with Gaussian noise of magnitude 0.001 at each iteration.

Original: The data augmentation transform consists of color augmentation of strength 1.0 from (Chen et al., 2020), as a random horizontal crop, and a image resize between 0.3 and 1.0 and a Gaussian blur of 5 pixels.
Revised: The data augmentation transform consists of color augmentation of strength 1.0 from (Chen et al., 2020), as a random horizontal crop, and a image resize between 0.3 and 1.0 and a Gaussian blur of 5 pixels.

Original: CelebA/LSUN Bed
Revised: CelebA/LSUN Bed

Original: For CelebA and LSUN bed datasets, we use 40 steps of Langevin sampling to generate negative samples.
Revised: For CelebA and LSUN bed datasets, we use 40 steps of Langevin sampling to generate negative samples.

Original: The Langevin sampling step size is set to be 1000, with Gaussian noise of magnitude 0.001 applied at each iteration.
Revised: The Langevin sampling step size is set to be 1000, with Gaussian noise of magnitude 0.001 applied at each iteration.

Original: The data augmentation transform consists of color augmentation of strength 0.5 from (Chen et al., 2020), as a random horizontal crop, and a image resize between 0.3 and 1.0 and a Gaussian blur of 11 pixels.A. A BLATIONS
Revised: The data augmentation transform consists of color augmentation of strength 0.5 from (Chen et al., 2020), as a random horizontal crop, and a image resize between 0.3 and 1.0 and a Gaussian blur of 11 pixels.

Original: We run ablations of each of the proposed components of our model.

Original: In Table 3, we ﬁnd that each proposed component of our framework leads to improved qualitative performance.

Original: [Table]

Original: A. C OMPARISON OF CD/KL G RADIENT M AGNITUDES
Revised: A. C OMPARISON OF CD/KL G RADIENT M AGNITUDES

Original: We plot the overall gradient magnitudes of the contrastive divergence and KL loss terms during training of an EBM in Figure 11.
Revised: We plot the overall gradient magnitudes of the contrastive divergence and KL loss terms during training of an EBM in Figure 12.

Original: We ﬁnd that relative magnitude of both training remains constant across training, and that the gradient of the KL objective is non-negligible. 
Revised: We ﬁnd that relative magnitude of both training remains constant across training, and that the gradient of the KL objective is non-negligible. A. A NALYSIS OF T RUNCATED L ANGEVIN B ACKPROPAGATION

Revised: To test the effect to truncating backpropogation through the KL loss to only thesample step of Langevin sampling, we train two seperate models on MNIST, one with backpropogation through all Langevin

Original: [Figure]
Revised: [Figure]

Revised: steps, and one with backpropogation through the last Langevin step.

Revised: We obtain FIDs of 90.54 with backpropogation through 1 step of Langevin sampling and FIDs of 94.85 with backpropogation through all steps on Langevin sampling.

Revised: We present illustrations of samples generated with one step in Figure 13 and with all steps in Figure 14.

Revised: Langevin , we train two seperate models in MNIST, one with backpropogation, We experiment with using and back-propogation through all the step

Revised: A. A NALYSIS OF E FFECT OF KL L OSS ON M ODE E XPLORATION

Revised: The KL loss adds an additional term to EBM training, that encourages EBM training updates to maintain good mode coverage while optimizing the usual contrastive divergence objective.

Revised: Thus the

Revised: KL loss serves as a regularizer to prevent EBM sampling from collapsing.

Revised: In the absence of the KL loss, EBM sampling collapses and generates samples in Figure

Revised: [Figure]

Revised: A. C OMPARISON TO O THER C OMPOSITIONAL G ENERATIVE M ODELS

Revised: To our knowledge, there are relatively few other models that can compositionally combine, with the approach of JVAE (Vedantam et al., 2018) being the closest to our work.

Revised: We provide comparisons in

Revised: Figure 16.

Revised: Our approach is signiﬁcantly less blurry than JVAE.

Original: A. A DDITIONAL Q UALITATIVE I MAGES
Revised: A. A DDITIONAL Q UALITATIVE I MAGES

Original: We present randomly generated qualitative images on the LSUN dataset in Figure 12 and the CIFAR10 dataset in Figure 13.
Revised: We present randomly generated qualitative images on the LSUN dataset in Figure 17 and the CIFAR10 dataset in Figure 18.

Original: In both setting, we ﬁnd that unconditional images appear mostly globally coherent.
Revised: In both setting, we ﬁnd that unconditional images appear mostly globally coherent.