Conditional Image Generation On Cifar 10

FID

Intra-FID

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	FID	Intra-FID
cgans-with-auxiliary-discriminative	5.66	40.45
cgans-with-projection-discriminator	17.5	-
denoising-likelihood-score-matching-for-1	2.25	-
feature-quantization-improves-gan-training	5.34	-
training-generative-adversarial-networks-with-2	2.42	-
lessons-learned-from-the-training-of-gans-on	3.6	-
improved-techniques-for-training-gans	-	-
improved-training-of-wasserstein-gans	-	-
rebooting-acgan-auxiliary-classifier-gans	2.26	-
unsupervised-representation-learning-with-1	-	-
stacked-generative-adversarial-networks	-	-
adaptive-weighted-discriminator-for-training	6.89	-
contrastive-generative-adversarial-networks	10.30	-
adaptive-weighted-discriminator-for-training	8.03	-
calibrating-energy-based-generative	-	-
consistency-regularization-for-generative-1	11.67	-
learning-to-draw-samples-with-application-to	-	-
class-splitting-generative-adversarial	-	-
deep-polynomial-neural-networks	36.77	-
large-scale-gan-training-for-high-fidelity	14.73	-
refining-generative-process-with	1.64	-
conditional-image-synthesis-with-auxiliary	-	-
negative-data-augmentation-1	9.42	-
lr-gan-layered-recursive-generative	-	-
cgans-with-multi-hinge-loss	7.5	-