Adversarial Feature Learning introduces Bidirectional Generative Adversarial Networks (BiGANs) to learn inverse mappings from data to latent spaces, enabling useful feature representations for auxiliary tasks. GANs, while effective at generating data, lack an inverse mapping. BiGANs extend GANs by adding an encoder to learn this inverse mapping, allowing for competitive feature learning in both supervised and unsupervised settings. The framework ensures that the encoder and generator are bijective and inverses of each other, making BiGANs robust and versatile for various data types. BiGANs are shown to be effective in learning meaningful features for tasks like image classification and object detection, outperforming baselines such as latent regressors and discriminators. Theoretical analysis confirms that BiGANs optimize Jensen-Shannon divergence between joint distributions, leading to strong feature representations. Empirical results on datasets like MNIST and ImageNet demonstrate BiGANs' effectiveness in learning features that generalize well to auxiliary tasks.Adversarial Feature Learning introduces Bidirectional Generative Adversarial Networks (BiGANs) to learn inverse mappings from data to latent spaces, enabling useful feature representations for auxiliary tasks. GANs, while effective at generating data, lack an inverse mapping. BiGANs extend GANs by adding an encoder to learn this inverse mapping, allowing for competitive feature learning in both supervised and unsupervised settings. The framework ensures that the encoder and generator are bijective and inverses of each other, making BiGANs robust and versatile for various data types. BiGANs are shown to be effective in learning meaningful features for tasks like image classification and object detection, outperforming baselines such as latent regressors and discriminators. Theoretical analysis confirms that BiGANs optimize Jensen-Shannon divergence between joint distributions, leading to strong feature representations. Empirical results on datasets like MNIST and ImageNet demonstrate BiGANs' effectiveness in learning features that generalize well to auxiliary tasks.