MaCo-GAN introduces a manifold-contrastive GAN that replaces adversarial loss with a contrastive minimax game over synthesized fake samples to improve the perception-distortion trade-off in SISR.
TripletGAN: Training Generative Model with Triplet Loss
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts. The main innovation of triplet loss is using feature map to replace softmax in the classification task. Inspired by this concept, we propose here a new adversarial modeling method by substituting the classification loss of discriminator with triplet loss. Theoretical proof based on IPM (Integral probability metric) demonstrates that such setting will help the generator converge to the given distribution theoretically under some conditions. Moreover, since triplet loss requires the generator to maximize distance within a class, we justify tripletGAN is also helpful to prevent mode collapse through both theory and experiment.
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
MaCo-GAN introduces a manifold-contrastive GAN that replaces adversarial loss with a contrastive minimax game over synthesized fake samples to improve the perception-distortion trade-off in SISR.