699 research outputs found
F?D: On understanding the role of deep feature spaces on face generation evaluation
Perceptual metrics, like the Fr\'echet Inception Distance (FID), are widely
used to assess the similarity between synthetically generated and ground truth
(real) images. The key idea behind these metrics is to compute errors in a deep
feature space that captures perceptually and semantically rich image features.
Despite their popularity, the effect that different deep features and their
design choices have on a perceptual metric has not been well studied. In this
work, we perform a causal analysis linking differences in semantic attributes
and distortions between face image distributions to Fr\'echet distances (FD)
using several popular deep feature spaces. A key component of our analysis is
the creation of synthetic counterfactual faces using deep face generators. Our
experiments show that the FD is heavily influenced by its feature space's
training dataset and objective function. For example, FD using features
extracted from ImageNet-trained models heavily emphasize hats over regions like
the eyes and mouth. Moreover, FD using features from a face gender classifier
emphasize hair length more than distances in an identity (recognition) feature
space. Finally, we evaluate several popular face generation models across
feature spaces and find that StyleGAN2 consistently ranks higher than other
face generators, except with respect to identity (recognition) features. This
suggests the need for considering multiple feature spaces when evaluating
generative models and using feature spaces that are tuned to nuances of the
domain of interest.Comment: Code and dataset to be released soo
Controllable Image Generation via Collage Representations
Recent advances in conditional generative image models have enabled
impressive results. On the one hand, text-based conditional models have
achieved remarkable generation quality, by leveraging large-scale datasets of
image-text pairs. To enable fine-grained controllability, however, text-based
models require long prompts, whose details may be ignored by the model. On the
other hand, layout-based conditional models have also witnessed significant
advances. These models rely on bounding boxes or segmentation maps for precise
spatial conditioning in combination with coarse semantic labels. The semantic
labels, however, cannot be used to express detailed appearance characteristics.
In this paper, we approach fine-grained scene controllability through image
collages which allow a rich visual description of the desired scene as well as
the appearance and location of the objects therein, without the need of class
nor attribute labels. We introduce "mixing and matching scenes" (M&Ms), an
approach that consists of an adversarially trained generative image model which
is conditioned on appearance features and spatial positions of the different
elements in a collage, and integrates these into a coherent image. We train our
model on the OpenImages (OI) dataset and evaluate it on collages derived from
OI and MS-COCO datasets. Our experiments on the OI dataset show that M&Ms
outperforms baselines in terms of fine-grained scene controllability while
being very competitive in terms of image quality and sample diversity. On the
MS-COCO dataset, we highlight the generalization ability of our model by
outperforming DALL-E in terms of the zero-shot FID metric, despite using two
magnitudes fewer parameters and data. Collage based generative models have the
potential to advance content creation in an efficient and effective way as they
are intuitive to use and yield high quality generations
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