Original: C ROSS -L INKED V ARIATIONAL A UTOENCODERS FOR G ENERALIZED Z ERO  L EARNINGAnonymous authors
Revised: C ROSS -L INKED V ARIATIONAL A UTOENCODERS FOR G ENERALIZED Z ERO -S HOT L EARNING

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artiﬁcial image features.
Revised: Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artiﬁcial image features.

Original: However, learning a shared cross-modal embedding by aligning the latent spaces of modality-speciﬁc autoencoders is shown to be promising in (generalized) zero-shot learning.
Revised: However, learning a shared cross-modal embedding by aligning the latent spaces of modality-speciﬁc autoencoders is shown to be promising in (generalized) zero-shot learning.

Original: While following the same direction, we also take artiﬁcial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classiﬁer.
Revised: While following the same direction, we also take artiﬁcial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders for the purpose of generating latent features to train a softmax classiﬁer.

Original: We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shotas well as on few-shot learning.
Revised: We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shot learning.

Original: Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings1 .
Revised: Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Generalized zero-shot learning (GZSL) is a classiﬁcation task where no labeled training examples are available from some of the classes.
Revised: Generalized zero-shot learning (GZSL) is a classiﬁcation task where no labeled training examples are available from some of the classes.

Original: Many approaches learn a mapping between images and their class embeddings Frome et al.
Revised: Many approaches learn a mapping between images and their class embeddings (Frome et al., 2013;

Original: ; Akata et al.
Revised: Akata et al., 2016;

Original: ; Norouzi et al. ; Xian et al.
Revised: Norouzi et al., 2013; Xian et al., 2016;

Original: Akata et al.
Revised: Akata et al., 2015).

Original: For instance, ALE Akata et al. (2016) maps CNN features of images to a perclass attribute space.
Revised: For instance, ALE (Akata et al., 2016) maps CNN features of images to a perclass attribute space.

Original: An orthogonal approach to GZSL is to augment data by generating artiﬁcial image features, such as Xian et al.
Revised: An orthogonal approach to GZSL is to augment data by generating artiﬁcial image features, such as Xian et al.

Original: (2018b) who proposed to generate image features via a conditional WGAN.
Revised: (2018b) who proposed to generate image features via a conditional WGAN.

Original: As a third approach, Tsai et al.
Revised: As a third approach, Tsai et al.

Original: (2017) proposed to learn a latent space embedding by transforming both modalities to the latent spaces of autoencoders and match the corresponding distributions by minimizing the Maximum Mean Discrepancy (MMD).
Revised: (2017) proposed to learn a latent space embedding by transforming both modalities to the latent spaces of autoencoders and match the corresponding distributions by minimizing the Maximum Mean Discrepancy (MMD).

Original: Learning such cross-modal embeddings can be beneﬁcial for potential downstream tasks that require multimodal fusion.
Revised: Learning such cross-modal embeddings can be beneﬁcial for potential downstream tasks that require multimodal fusion.

Original: In this regard, Ramakrishnan et al.
Revised: In this regard, Ramakrishnan et al.

Original: (2017) recently used a cross-modal autoencoder to extend visual question answering to previously unseen objects.
Revised: (2017) recently used a cross-modal autoencoder to extend visual question answering to previously unseen objects.

Original: Although recent cross-modal autoencoder architectures represent class prototypes in a latent space Mukherjee et al. ; Tsai et al. (2017), better generalization can be achieved if the shared representation space is more amenable to interpolation between different classes.
Revised: Although recent cross-modal autoencoder architectures represent class prototypes in a latent space (Mukherjee et al., 2017; Tsai et al., 2017), better generalization can be achieved if the shared representation space is more amenable to interpolation between different classes.

Original: Variational Autoencoders (VAEs) are known for their capability in accurate interpolation between representations in their latent space, i.e. as demonstrated for sentence interpolation Bowman et al. and image interpolation Higgins et al.
Revised: Variational Autoencoders (VAEs) are known for their capability in accurate interpolation between representations in their latent space, i.e. as demonstrated for sentence interpolation (Bowman et al., 2015) and image interpolation (Higgins et al., 2016).

Original: Hence, in this work, we train VAEs to encode and decode features from different modalities, and align their latent spaces  matching the parametrized latent distributions and by enforcing a cross-modal reconstruction criterion.
Revised: Hence, in this work, we train VAEs to encode and decode features from different modalities, and align their latent spaces by matching the parametrized latent distributions and by enforcing a cross-modal reconstruction criterion.

Original: Since we learn representations that are oblivious to their origin, a zero-shot visual classiﬁer can be trained using latent space features from semantic data.
Revised: Since we learn representations that are oblivious to their origin, a zero-shot visual classiﬁer can be trained using latent space features from semantic data.

Original: Our contributions in this work are as follows.
Revised: Our contributions in this work are as follows

Original: (1) We propose a model that learns shared cross-modal latent representations of multiple data modalities using simple VAEs via distribution alignment and cross alignment objectives.
Revised: (1) We propose a model that learns shared cross-modal latent representations of multiple data modalities using simple VAEs via distribution alignment and cross alignment objectives.

Original: (2) Our model establishes the new state-of-the-art performance on generalized zero-shotand few-shot learning settings on conventional benchmark datasets.
Revised: (2) Our model establishes the new state-of-the-art performance on generalized zero-shot settings on conventional benchmark datasets.

Original: (3) Finally, we show that the latent features learned by our model improve the state of the art in the truly large-scale ImageNet dataset in all splits for the generalized zero-shot learning task.
Revised: (3) Finally, we show that the latent features learned by our model improve the state of the art in the truly large-scale ImageNet dataset in all splits for the generalized zero-shot learning task.

Original: M ETHOD
Revised: M ETHOD

Original: E 
Revised: E E D D

Original: E 
Revised: E E D D

Revised: E

Revised: E

Original: D D
Revised: D D

Original: E E
Revised: E E

Original: D 
Revised: D D E E

Original: D 
Revised: D D E E

Original: E E D D
Revised: D

Original: E E D D
Revised: D

Original: E E
Revised: E E

Original: DD E E
Revised: D

Original: D
Revised: D

Original: D

Original: E

Original: E D D COMPACT FIGURES (SMALL ENOUGH TO PUT 3 IN A ROW) SLIGHTLY MORE DETAILED FIGURES (PROBABLY TOO BIG TO PUT 3 IN A ROW) DETAILED FIGURE (THE EQUATIONS ON THE RIGHT ARE THE CROSS-RECONSTRUCTION LOSS.
Revised: COMPACT FIGURES (SMALL ENOUGH TO PUT 3 IN A ROW) SLIGHTLY MORE DETAILED FIGURES (PROBABLY TOO BIG TO PUT 3 IN A ROW) DETAILED FIGURE (THE EQUATIONS ON THE RIGHT ARE THE CROSS-RECONSTRUCTION LOSS.

Original: THE BASIC VAE LOSS IS NOT SHOWN)
Revised: THE BASIC VAE LOSS IS NOT SHOWN)

Original: CADA-VAE:
Revised: CADA-VAE:

Original: DA-VAE:
Revised: DA-VAE:

Original: CA-VAE:
Revised: CA-VAE:

Original: Current 
Revised: Current choice:

Original: [Figure]
Revised: [Figure]

Original: Generalized Zero-shot Learning Let S =
Revised: Generalized Zero-shot Learning Let S =

Original: { ( x, y, c ( y ))
Revised: { ( x, y, c ( y ))

Original: | x ∈ X, y ∈ Y S , c ( y ) ∈ C } be a set of training examples, consisting of image-features x , e.g. extracted by a CNN, class labels y available during training and class-embeddings c ( y ) .
Revised: | x ∈ X, y ∈ Y S , c ( y ) ∈ C } be a set of training examples, consisting of image-features x , e.g. extracted by a CNN, class labels y available during training and class-embeddings c ( y ) .

Original: Typical class-embeddings are vectors of continuous attributes or Word2Vec features Mikolov et al.
Revised: Typical class-embeddings are vectors of continuous attributes or Word2Vec features (Mikolov et al., 2013).

Original: In addition, an set U = { ( u, c ( u )) | u ∈
Revised: In addition, an set U = { ( u, c ( u )) | u ∈

Original: Y u , c ( u ) ∈ C } is used, where u denote unseen classes from a set Y u , which is disjoint from Y S .
Revised: Y u , c ( u ) ∈ C } is used, where u denote unseen classes from a set Y u , which is disjoint from Y S .

Original: Here, C ( U ) =
Revised: Here, C ( U ) =

Original: { c ( u 1 ) , ..., c ( u L ) } is the set of class-embeddings of unseen classes.
Revised: { c ( u 1 ) , ..., c ( u L ) } is the set of class-embeddings of unseen classes.

Original: In the legacy challenge of ZSL, the task is to learn a classiﬁer f ZSL :
Revised: In the legacy challenge of ZSL, the task is to learn a classiﬁer f ZSL :

Original: X → Y U .
Revised: X → Y U .

Original: However, in this work, we focus on the more realistic and challenging setup of generalized zero-shot learning (GZSL) where the aim is to learn a classiﬁer f GZSL :
Revised: However, in this work, we focus on the more realistic and challenging setup of generalized zero-shot learning (GZSL) where the aim is to learn a classiﬁer f GZSL :

Original: X → Y U ∪ Y S .
Revised: X → Y U ∪ Y S .

Original: The Objective Function CADA-VAE is trained with pairs of image features and attribute vectors of seen classes.
Revised: The Objective Function CADA-VAE is trained with pairs of image features and attribute vectors of seen classes.

Original: The data of each pair has to belong to the same class.
Revised: The data of each pair has to belong to the same class.

Original: In this process, an image feature encoder and an attribute encoder learn to transform the training data into the shared latent space.
Revised: In this process, an image feature encoder and an attribute encoder learn to transform the training data into the shared latent space.

Original: The encoders belong to two VAEs with a common latent space.
Revised: The encoders belong to two VAEs with a common latent space.

Original: Once the VAEs are trained, a softmax classiﬁer is trained on both seen image data and unseen attributes, after they are transformed into the latent representation.
Revised: Once the VAEs are trained, a softmax classiﬁer is trained on both seen image data and unseen attributes, after they are transformed into the latent representation.

Original: As the VAE encoding is non-deterministic, many latent features are sampled for each datapoint.
Revised: As the VAE encoding is non-deterministic, many latent features are sampled for each datapoint.

Original: Since we only have one attribute vector per class, we oversample latent-space encoded features of unseen classes.
Revised: Since we only have one attribute vector per class, we oversample latent-space encoded features of unseen classes.

Original: To test the classiﬁer, the visual test data is ﬁrst transformed into the latent space, using only the predicted means µ of the latent representation.
Revised: To test the classiﬁer, the visual test data is ﬁrst transformed into the latent space, using only the predicted means µ of the latent representation.

Original: The Objective function for training the VAEs is derived as follows.
Revised: The Objective function for training the VAEs is derived as follows.

Original: For every modality i (image features, attributes), a VAE is trained.
Revised: For every modality i (image features, attributes), a VAE is trained.

Original: The basic VAE loss for a feature x of modality i ∈ 1 , 2 , ..
Revised: The basic VAE loss for a feature x of modality i ∈ 1 , 2 , ..

Original: M is:
Revised: M is:

Original: [Equation]
Revised: [Equation]

Original: p [Equation] where D KL represents the Kullback-Leibler Divergence, β is a weight, q ( z | x ( i ) )
Revised: where D KL represents the Kullback-Leibler Divergence, β is a weight, q ( z | x ( i ) )

Original: =
Revised: =

Original: N ( µ, Σ) is the VAE encoder consisting of a multilayer perceptron, and p ( z ) is a Gaussian prior.
Revised: N ( µ, Σ) is the VAE encoder consisting of a multilayer perceptron, and p ( z ) is a Gaussian prior.

Original: Additionally, each encoded datapoint is decoded into every available modality, e.g. encoded image features are decoded into attributes and vice versa.
Revised: Additionally, each encoded datapoint is decoded into every available modality, e.g. encoded image features are decoded into attributes and vice versa.

Original: Consequently, we minimize the L1 cross-reconstruction loss:
Revised: Consequently, we minimize the L1 cross-reconstruction loss:

Original: [Equation]
Revised: [Equation]

Original: where γ is a weight.
Revised: where γ is a weight.

Revised: The L1 loss empirically proved to provide slighthly better results than L2.

Original: Furthermore, the 2-Wasserstein W distance between the multivariate Gaussian latent distribution of image features and attributes is minimized:[-[Equation] ; with W 2 ij = || µ i − µ j || + || Σ 
-]
Revised: Furthermore, the 2-Wasserstein W distance between the multivariate Gaussian latent distribution of image features and attributes is minimized:[-[Equation] ; with W 2 ij = || µ i − µ j || + || Σ 
-]

Original: i − Σ

Original: [Equation]

Original: || 2 Frobenius .

Original: [Equation]
Revised: [Equation]

Original: The VAE is trained using the ﬁnal objective L = L basic + L CA + L DA .
Revised: The VAE is trained using the ﬁnal objective L = L basic + L CA + L DA .

Original: We refer to the Cross-Aligned and Distribution-Aligned VAE as CADA-VAE.
Revised: We refer to the Cross-Aligned and Distribution-Aligned VAE as CADA-VAE.

Original: In addition, we test the variant L =
Revised: In addition, we test the variant L =

Original: L basic + L CA , termed CA-VAE, and the variant L = L basic + L DA , referred to as DA-VAE.
Revised: L basic + L CA , termed CA-VAE, and the variant L = L basic + L DA , referred to as DA-VAE.

Original: A latent size of 64 is used for all experiments, except 128 for ImageNet.
Revised: A latent size of 64 is used for all experiments, except 128 for ImageNet.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: We evaluate our framework on zero-shot learning benchmark datasets CUB-200-2011 Welinder et al. (2010) (CUB), SUN attribute (SUN) Patterson & Hays (2012), Animals with Attributes 1 and(AWA1 Lampert et al. AWA2 Xian et al. (2018a))
Revised: We evaluate our framework on zero-shot learning benchmark datasets CUB-200-2011 (Welinder et al., 2010), SUN attribute (Patterson & Hays, 2012), AwA 1 and 2 (Lampert et al., 2009; Xian et al., 2018a) for the GZSL setting.

Original: for the generalized zero-shot learning (GZSL) and generalized few-shot learning (GFSL) settings.

Original: All image features used for training the VAEs are extracted from the 2048 -dimensional ﬁnal pooling layer of a ResNet - 101 .
Revised: All image features used for training the VAEs are extracted from the 2048 -dimensional ﬁnal pooling layer of a ResNet - 101 .

Original: To avoid violating the zero-shot assumption, i.e. test classes need to be disjoint from the classes that ResNet101 was trained with, we use the proposed training splits in Xian et al.
Revised: To avoid violating the zero-shot assumption, i.e. test classes need to be disjoint from the classes that ResNet - 101 was trained with, we use the proposed training splits in Xian et al.

Original: (2018a).
Revised: (2018a).

Original: As class embeddings, attribute vectors were utilized if available.
Revised: As class embeddings, attribute vectors were utilized if available.

Original: For ImageNet we used Word2VecMikolov et al.
Revised: For ImageNet we used Word2Vec

Original: (2013) embeddings provided by Changpinyo et al.
Revised: (Mikolov et al., 2013) embeddings provided by Changpinyo et al.

Original: All hyperparameters were chosen on a validation set provided by Xian et al.
Revised: All hyperparameters were chosen on a validation set provided by Xian et al.

Original: (2018a).
Revised: (2018a).

Original: We report the harmonic mean (H) between seen (S) and unseen (U) average per-class accuracy, i.e. the Top-1 accuracy is averaged on a per-class basis.
Revised: We report the harmonic mean (H) between seen (S) and unseen (U) average per-class accuracy, i.e. the Top-1 accuracy is averaged on a per-class basis.

Original: Generalized Zero-Shot Learning We compare our model with 11 state-of-the-art models.
Revised: Generalized Zero-Shot Learning We compare our model with 11 state-of-the-art models.

Original: Among those, CVAE Mishra et al. (2017), SE Verma et al. (2017), and f-CLSWGAN Xian et al. (2018b) learn to generate artiﬁcial visual data and thereby treat the zero-shot problem as a data-augmentation problem.
Revised: Among those, CVAE (Mishra et al., 2017), SE (Verma et al., 2017), and f-CLSWGAN (Xian et al., 2018b) learn to generate artiﬁcial visual data and thereby treat the zero-shot problem as a data-augmentation problem.

Original: On the other hand, the classic ZSL methods DeViSE Frome et al.

Original: (2013), SJE Akata et al. (2015), ALE Akata et al. (2016), EZSL Romera-Paredes & Torr (2015) and LATEM Xian et al. (2016) use a linear compatibility function or other similarity metrics to compare embedded visual and semantic features; CMT Socher et al.
Revised: On the other hand, the classic ZSL methods DeViSE (Frome et al., 2013), SJE (Akata et al., 2015), ALE (Akata et al., 2016), EZSL (Romera-Paredes & Torr, 2015) and LATEM (Xian et al., 2016) use a linear compatibility function or other similarity metrics to compare embedded visual and semantic features; CMT (Socher et al., 2013) and LATEM (Xian et al., 2016) utilize multiple neural networks to learn a non-linear embedding; and SYNC (Changpinyo et al., 2016) learns by aligning a class embedding space and a weighted bipartite graph.

Original: and

Original: LATEM Xian et al.

Original: (2016) utilize multiple neural networks to learn a non-linear embedding; and SYNC Changpinyo et al.

Original: (2016) learns by aligning a class embedding space and a weighted bipartite graph.

Original: ReViSE Tsai et al. (2017) proposes a shared latent manifold learning using an autoencoder between the image features and class attributes.
Revised: ReViSE (Tsai et al., 2017) proposes a shared latent manifold learning using an autoencoder between the image features and class attributes.

Original: The results in Table 1 show that our CADA-VAE outperforms all other methods on all datasets.
Revised: The results in Table 1 show that our CADA-VAE outperforms all other methods on all datasets.

Original: Moreover, our model achieves signiﬁcant improvements over feature generating models most notably on CUB.
Revised: Moreover, our model achieves signiﬁcant improvements over feature generating models most notably on CUB.

Original: Compared to the classic ZSL methods, our method leads to at least 100% improvement in harmonic mean accuracies.
Revised: Compared to the classic ZSL methods, our method leads to at least 100% improvement in harmonic mean accuracies.

Original: In the legacy challenge of ZSL setting, which is hardly realistic, our CADA-VAE provides competitive performance, i.e. 60 . 4 on CUB, 61 . 8 on SUN, 62 . 3 on
Revised: In the legacy challenge of ZSL setting, which is hardly realistic, our CADA-VAE provides competitive performance, i.e. 60 . 4 on CUB, 61 . 8 on SUN, 62 . 3 on

Original: AWA1, 64 . 0 on AWA2.
Revised: AWA1, 64 . 0 on AWA2.

Original: However, in this work, we focus on the more practical and challenging
Revised: However, in this work, we focus on the more practical and challenging

Original: GZSL setting.
Revised: GZSL setting.

Original: We believe the obtained increase in performance by our model can be explained as follows.
Revised: We believe the obtained increase in performance by our model can be explained as follows.

Revised: CADA

Original: CADA-VAE learns a shared representation in a weakly supervised fashion, through a crossreconstruction objective.
Revised: VAE learns a shared representation in a weakly supervised fashion, through a crossreconstruction objective.

Original: Since the latent features have to be decoded into every involved modality, and since every modality encodes complementary information, the model is encouraged to learn an encoding that retains the information contained in all used modalities.
Revised: Since the latent features have to be decoded into every involved modality, and since every modality encodes complementary information, the model is encouraged to learn an encoding that retains the information contained in all used modalities.

Original: In doing so, our method is less biased towards learning the distribution of the seen class image features, which is known as the projection domain shift problem Fu et al.
Revised: In doing so, our method is less biased towards learning the distribution of the seen class image features, which is known as the projection domain shift problem (Fu et al., 2014).

Original: As we generate a certain number of latent features per class using non-deterministic encoders, our method is also akin to data-generating approaches.
Revised: As we generate a certain number of latent features per class using non-deterministic encoders, our method is also akin to data-generating approaches.

Original: However, the learned representations lie in a lower dimensional space, i.e. only 64 , and therefore, are less prone to bias towards the training set of image features.
Revised: However, the learned representations lie in a lower dimensional space, i.e. only 64 , and therefore, are less prone to bias towards the training set of image features.

Original: In effect, our training is more stable than the adversarial training schemes used for data generation Xian et al. (2018b).
Revised: In effect, our training is more stable than the adversarial training schemes used for data generation (Xian et al., 2018b).

Original: ImageNet Experiments In Xian et al.
Revised: ImageNet Experiments In Xian et al.

Original: (2018a) several evaluation splits were proposed with increasing granularity and size both in terms of the number of classes and the number of images.
Revised: (2018a) several evaluation splits were proposed with increasing granularity and size both in terms of the number of classes and the number of images.

Original: Note that since all the images of 1K classes are used to train ResNet - 101 , measuring seen class accuracies would be biased.
Revised: Note that since all the images of 1K classes are used to train ResNet - 101 , measuring seen class accuracies would be biased.

Original: However, we can still evaluate the accuracy of unseen class images in the GZSL search space that contains both seen and unseen classes.
Revised: However, we can still evaluate the accuracy of unseen class images in the GZSL search space that contains both seen and unseen classes.

Original: Hence, at test time the 1 K seen classes act as distractors.
Revised: Hence, at test time the 1 K seen classes [Table] ReViSE [Table]

Revised: [Table]

Revised: CADA-

Original:  For ImageNet, as attributes are not available, we use Word2Vec features as class embeddings provided by Changpinyo et al.
Revised: [Table] [Figure] act as distractors. For ImageNet, as attributes are not available, we use Word2Vec features as class embeddings provided by Changpinyo et al.

Original: We compare our model with f-CLSWGAN Xian et al. (2018b), i.e. an image feature generating framework which currently achieves the state of the art on ImageNet.
Revised: We compare our model with f-CLSWGAN (Xian et al., 2018b), i.e. an image feature generating framework which currently achieves the state of the art on ImageNet.

Original: We use the same evaluation protocol on all the splits.
Revised: We use the same evaluation protocol on all the splits.

Original: Among the splits, 2H and[Table] [Figure] 3H are the classes 2 or 3 hops away from the 1 K seen training classes of ImageNet according to the
Revised: Among the splits, 2H and 3H are the classes 2 or 3 hops away from the 1 K seen training classes of ImageNet according to the

Original: ImageNet hierarchy.
Revised: ImageNet hierarchy.

Original: M 500 , M 1 K and M 5 K are the 500 , 1000 and 5000 most populated classes, while L500, L1K and L5K are the 500 , 1000 and 5000 least populated classes that come from the rest of the 21K classes.
Revised: M 500 , M 1 K and M 5 K are the 500 , 1000 and 5000 most populated classes, while L500, L1K and L5K are the 500 , 1000 and 5000 least populated classes that come from the rest of the 21K classes.

Original: Finally, ‘All‘ denotes the remaining 20 K classes of ImageNet.
Revised: Finally, ‘All‘ denotes the remaining 20 K classes of ImageNet.

Original: As shown in Figure 2, our model signiﬁcantly improves the state of the art in all the available splits.
Revised: As shown in Figure 2, our model signiﬁcantly improves the state of the art in all the available splits.

Original: Note that the test time search space in the ‘All‘ split is 22K dimensional.
Revised: Note that the test time search space in the ‘All‘ split is 22K dimensional.

Original: Hence even a small improvement in accuracy on this split is considered to be compelling.
Revised: Hence even a small improvement in accuracy on this split is considered to be compelling.

Original: The achieved substantial increase in performance by CADA-VAE shows that our 128 -dim latent feature space constitutes a robust generalizable representation, surpassing the current state-of-the-art image feature generating framework f-CLSWGAN .
Revised: The achieved substantial increase in performance by CADA-VAE shows that our 128 -dim latent feature space constitutes a robust generalizable representation, surpassing the current state-of-the-art image feature generating framework f-CLSWGAN .

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this work, we propose CADA-VAE , a cross-modal embedding framework for generalized zeroand few-shot learning in which the modality-speciﬁc latent distributions are aligned by minimizing their Wasserstein distance and by using cross-reconstruction.
Revised: In this work, we propose CADA-VAE , a cross-modal embedding framework for generalized zeroshot learning in which the modality-speciﬁc latent distributions are aligned by minimizing their Wasserstein distance and by using cross-reconstruction.

Original: This procedure leaves us with encoders that can encode features from different modalities into one cross-modal embedding space, in which a linear softmax classiﬁer can be trained.
Revised: This procedure leaves us with encoders that can encode features from different modalities into one cross-modal embedding space, in which a linear softmax classiﬁer can be trained.

Original: We present different variants of cross-aligned and distribution aligned VAEs and establish new state-of-the-art results in generalized zero-shot learning for four medium-scale benchmark datasets as well as the large-scale ImageNet.
Revised: We present different variants of cross-aligned and distribution aligned VAEs and establish new state-of-the-art results in generalized zero-shot learning for four medium-scale benchmark datasets as well as the large-scale ImageNet.

Original: We further show that across-modal embedding model for generalized zero-shot learning achieves better performance than data-generating methods, establishing the new state of the art.
Revised: We further show that a