Variational Autoencoders (VAEs) are a class of generative models primarily employed in machine learning. They synthesize novel data instances, partly founded on the principles of deep learning and variational inference, generating fascinating, feasible data samples.

VAEs exhibit specific characteristics:

• Deep Architectures: VAEs consist of deep architectures with meticulous design catering to machine learning and statistical inference, which enable the model to predict or generate diverse data instances.
• Unsupervised Learning: VAEs rely on unsupervised learning, implying that they do not need labeled data to learn valuable features in the data. This characteristic makes them especially appealing where acquiring labeled data proves challenging.
• Statistical Inference: VAEs incorporate variational inference principles, providing a plausible approach to performing complex probability computations, thus boosting efficiency.
• Embedded Code Space: VAEs embed the input information into a latent space and then rebuild the data, leveraging these codings to deliver generated samples with potent applications in data synthesis, anomaly detection, and content creation.

Variational Autoencoders have broad applicability across industries, especially where unsupervised learning is required or the focus is on generating new data samples from existing datasets.

Successfully implementing Variational Autoencoders starts with an organized, strategic approach. To build a high-performing VAE, developers should first understand and outline the specific tasks the model would address. They then need to determine suitable architectural choices, like the number of layers, latent space dimensions, and activation functions, followed by careful hyperparameter tuning. Adequate computation resources are crucial for efficient learning, especially with complex and sizable datasets. The implementation also requires persistent monitoring during training to swiftly identify and rectify performance inhibitors.

Importantly, one must remember that the selection and implementation of VAEs or any machine learning model remains intrinsically linked to the context of the task at hand. The specific problem's characteristics and constraints must guide model selection, with adequate evaluation of advantages and drawbacks for informed and astute decision-making.

Many organizations gravitate towards VAEs because they offer multiple benefits:

• Efficient Inference: By condensing complex probability calculations using variational inference, VAEs can generate usable data instances more efficiently. This feature makes them a preferable choice for tasks that involve sizable datasets.
• Unsupervised Learning: VAEs are particularly proficient at learning valuable features from unlabeled data, providing a cost-effective and time-efficient method of dealing with scenarios where gathering labeled data may be time-consuming or expensive.
• Data Generation: VAEs stand out for their ability to generate novel, valid data samples based on learnt features, making them a valuable tool in content creation, drug discovery, and other applications requiring data synthesis.
• An embedded code space: The architecture of VAEs enables them to compress the data in a lower-dimensional hidden space, and then decode this data for generating samples. This feature is crucial in applications such as data compression or anomaly detection.
• Regularized Reconstruction: Regularization factors within VAEs help control the complexity of data representation, enabling smoother interpolations that facilitate data sampling and learning useful representations.

Despite these advantages, Variational Autoencoders have potential shortcomings.

Certain factors pose challenges to the use of VAEs:

• Computational Costs: As a generative model, VAEs may require significant computational resources for complex datasets, making them less favorable for resource-limited settings.
• Model Complexity: VAEs, with their inherent Bayesian nature and the use of variational inference, present a learning curve for those not versed in advanced concepts. This characteristic may make adoption challenging for those new to deep learning and statistical inference.
• Inadequate Sharp Features: VAEs sometimes fail to generate crisp, sharp features due to the Gaussian assumption within its architecture. Hence, generated samples lack high-frequency information, rendering images blurry or text less coherent.
• Hyperparameter Tuning: The performance of VAE largely depends on accurate tuning of hyperparameters. Without a correct setting, there is a risk of poor performance or even model failure.