or using segmentation results. ◼ Related Works Goodfellow(2014) trained generator(G) on data distribution and a discriminator(D) to distinguish real from generated data, thus after optimization G can produce images indistinguishable from training examples. Mirza(2014) proposed conditional GAN generates images conditioned on information. ◼ Proposed Methodology The proposed algorithm uses images of person wearing a garment(x) and images of garment(y) for supervised training. The model(generator and discriminator) is trained adverbially as 𝑚𝑖𝑛𝐺 𝑚𝑎𝑥𝐷 𝐿𝑐𝐺𝐴𝑁 𝐺,𝐷 + 𝑦𝑖 𝐿𝑖𝑑 (𝐺) + 𝑦𝑐 𝐿𝑐𝑦𝑐 (𝐺) where 𝐿𝑐𝐺𝐴𝑁 𝐺, 𝐷 = 𝔼𝑥𝑖,𝑦𝑖∼𝑝𝑑𝑎𝑡𝑎 σ 𝜆,𝜇 [log𝐷𝜆 ,𝜇 (𝑥𝑖 ,𝑦𝑖 )] + 𝔼𝑥𝑖,𝑦𝑖,𝑦𝑗∼𝑝𝑑𝑎𝑡𝑎 σ 𝜆,𝜇 [(1 − log𝐷𝜆 ,𝜇 𝐺 𝑥𝑖 , 𝑦𝑖 , 𝑦𝑗 , 𝑦𝑗 )] + 𝔼𝑥𝑖,𝑦𝑗≠𝑖∼𝑝𝑑𝑎𝑡𝑎 σ 𝜆 ,𝜇 [(1 − log 𝐷𝜆 ,𝜇 𝑥𝑖 , 𝑦𝑖 )] for current garment 𝑦𝑖 and target garment 𝑦𝑗 . A regularization loss 𝐿𝑖𝑑 (𝐺) is used to avoid painting irrelevant regions as 𝐿𝑖𝑑 𝐺 = 𝔼𝑥𝑖 ,𝑦𝑖,𝑦𝑗∼𝑝𝑑𝑎𝑡𝑎 | 𝛼 𝑗 𝑖 | where . represents L1 normalization. To enforce consistency cycle loss 𝐿𝑐𝑦𝑐 (𝐺) is used as 𝐿𝑐𝑦𝑐 𝐺 = 𝔼𝑥 ,𝑦 ,𝑦 ∼𝑝 | 𝑥𝑖 − 𝐺 𝐺 𝑥𝑖 , 𝑦𝑖 , 𝑦𝑗 , 𝑦𝑗 ,𝑦𝑖 |. Thus, if 𝑥𝑗 = 𝐺(𝑥𝑖 , 𝑦𝑖 ,𝑦𝑗 ) modifies The Conditional Analogy GAN: Swapping Fashion Articles on People Images Copyright © 2022 VIVEN Inc. All Rights Reserved irrelevant regions reverse swapping as 𝐺(𝑥 𝑖 𝑗, 𝑦𝑗 , 𝑦𝑖 ) will generate image which when compared to 𝑥𝑖 will penalize the model. ◼ Results Zalandao dataset is used to evaluate the effectiveness of proposed algorithm. ◼ Next must-read paper: “A generative model of people in clothing ” ◼ Conclusion The performance can be further increased if foreground background segmentation is available, texture descriptors can further increase the performance of condition GAN.