In this paper, we propose a sketch and text guided probabilistic diffusion model for colored point cloud generation that conditions the denoising process jointly with a hand drawn sketch of the object and its textual description.
We formulate the model stability problem by studying how the predictions of a model change, even when it is retrained on the same data, as a consequence of stochasticity in the training process.
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
Semi-supervised learning is an efficient way to improve performance for natural language processing systems.