However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms, which can be challenging due to the non-convex nature of GAN loss functions. To address this, we propose a five-dimensional Gray wolf optimizer (5DGWO) to optimize GAN hyper...
Learning Combinatorial Optimization Algorithms over Graphs NIPS code 84 HashNet: Deep Learning to Hash by Continuation ICCV code 84 ECO: Efficient Convolution Operators for Tracking CVPR code 84 Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model NI...