Co-Evolutionary Compression for Unpaired Image Translation
By Han Shu, Yunhe Wang, Xu Jia, Kai Han, Hanting Chen, Chunjing Xu, Qi Tian. 2019
93 citations recorded by the source corpus.
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation. However, generators in these networks are of complicated architectures with large number of parameters and huge computational complexities. Existing methods are mainly designed for compressing and speeding-up deep neural networks in the classification task, and cannot be directly applied on GANs for image translation, due to their different objectives and training procedures. To this end, we develop a novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously. In practice, generators for two image domains are encoded as two populations and synergistically optimized for investigating the most important convolution filters iteratively. Fitness of each individual is calculated using the number of parameters, a discriminator-aware regularization, and the cycle consistency. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method for obtaining compact and effective generators.
Subfields: Cancer Research, Computer Vision and Pattern Recognition
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Citations in the corpus
- AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (2020, positive): “The current mainstream of model compression focuses on deep image classification or segmentation, which, as demonstrated in (Shu et al., 2019), cannot be easily extended to compress GANs (by default, defined as compressing their trained generators), That is mainly due to the vulnerability of their…”
- A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, methodology): “-CGAN) [175] for video question answering. 3.4 Miscellaneous GAN applications GANs have also been used in many other domains such as malware detection [176], chess game playing [177], network pruning [178, 179], spatial representation learning [180], mobile user profiling [181], data augmentation [1”
- Structured Pruning for Deep Convolutional Neural Networks: A Survey (2023, background): “Besides the prevailing CNNs, pruning is beneficial to other type of neural networks such as MLPs [217], rectifier neural networks [218], spiking neural networks [219], [220], and Generative adversarial network (GAN) [209], [210], [211], [212].”
- GAN Compression: Efficient Architectures for Interactive Conditional GANs (2020, methodology): “[60] proposed co-evolutionary pruning for CycleGAN by modifying the original CycleGAN algorithm.”
- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020, methodology): “Pruning [11, 23, 39, 25, 45, 47, 24] is used to reduce the redundant parameters in neural networks that are insensitive to the model performance.”
- CARS: Continuous Evolution for Efficient Neural Architecture Search (2019, background): “EA-based approaches (Xie and Yuille 2017; Real et al. 2019; Wang et al. 2018; Shu et al. 2019) search architectures with the help of evolutionary algorithms.”
- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, methodology): “GANs have been widely used in many other areas such as malware detection [434], chess game playing [435], steganography [436]–[439], privacy-preserving [440]–[442], social robot [443], and network pruning [444], [445].”
- Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts (2020, unknown): “Quantization [18, 19, 20, 21, 22, 23, 24] reduces the precision of the model weights, resulting in smaller model size and faster computation.”