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Unconditional image generation has recently been dominated by generativeadversarial networks (GANs). GAN methods train a generator which regressesimages from random noise vectors, as well as a discriminator that attempts todifferentiate between the generated images and a training set of real images.GANs have shown amazing results at generating realistic looking images. Despitetheir success, GANs suffer from critical drawbacks including: unstable trainingand mode-dropping. The weaknesses in GANs have motivated research intoalternatives including: variational auto-encoders (VAEs), latent embeddinglearning methods (e.g. GLO) and nearest-neighbor based implicit maximumlikelihood estimation (IMLE). Unfortunately at the moment, GANs stillsignificantly outperform the alternative methods for image generation. In thiswork, we present a novel method - Generative Latent Nearest Neighbors (GLANN) -for training generative models without adversarial training. GLANN combines thestrengths of IMLE and GLO in a way that overcomes the main drawbacks of eachmethod. Consequently, GLANN generates images that are far better than GLO andIMLE. Our method does not suffer from mode collapse which plagues GAN trainingand is much more stable. Qualitative results show that GLANN outperforms abaseline consisting of 800 GANs and VAEs on commonly used datasets. Our modelsare also shown to be effective for training truly non-adversarial unsupervisedimage translation.

Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors