The paper introduces Bidirectional Generative Adversarial Networks (BiGANs) as a novel unsupervised feature learning framework. BiGANs extend the traditional Generative Adversarial Networks (GANs) by adding an encoder to the generator and discriminator, enabling the learning of an inverse mapping from data to latent space. This allows BiGANs to learn useful feature representations that can be used for auxiliary supervised discrimination tasks. The authors demonstrate that BiGANs are competitive with contemporary approaches to unsupervised and weakly supervised feature learning, even on complex data distributions like natural images. The theoretical properties of BiGANs are explored, showing that the optimal generator and encoder are bijective and each other's inverses. Empirical results on datasets such as permutation-invariant MNIST and ImageNet validate the effectiveness of BiGANs in learning meaningful feature representations.The paper introduces Bidirectional Generative Adversarial Networks (BiGANs) as a novel unsupervised feature learning framework. BiGANs extend the traditional Generative Adversarial Networks (GANs) by adding an encoder to the generator and discriminator, enabling the learning of an inverse mapping from data to latent space. This allows BiGANs to learn useful feature representations that can be used for auxiliary supervised discrimination tasks. The authors demonstrate that BiGANs are competitive with contemporary approaches to unsupervised and weakly supervised feature learning, even on complex data distributions like natural images. The theoretical properties of BiGANs are explored, showing that the optimal generator and encoder are bijective and each other's inverses. Empirical results on datasets such as permutation-invariant MNIST and ImageNet validate the effectiveness of BiGANs in learning meaningful feature representations.