Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible. However, existing inversion methods usually suffer from the mode collapse problem, where the synthesized instances are highly similar to each other and thus show limited effectiveness for downstream tasks, such as knowledge distillation. In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. To this end, we introduce in CMI a contrastive learning objective that encourages the synthesizing instances to be distinguishable from the already synthesized ones in previous batches. Experiments of pre-trained models on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more visually plausible instances than the state of the arts, but also achieves significantly superior performance when the generated data are used for knowledge distillation. Code is available at \url{https://github.com/zju-vipa/DataFree}.

Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems' disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance. We close with alternative visions for a more equitable future for educational AI research.

Performance of recommender systems (RS) relies heavily on the Many large scale e-commerce platforms (such as Etsy, Overstock, amount of training data available. This poses a chicken-and-egg etc) and online content platforms (such as Spotify, Overstock, Disney, problem for early-stage products, whose amount of data, in turn, Netflix, etc) have such a large inventory of items that showcasing relies on the performance of their RS. On the other hand, zero-shot all of them in front of their users is simply not practical. In learning promises some degree of generalization from an old dataset particular, in the online content category of businesses, it is often to an entirely new dataset. In this paper, we explore the possibility seen that users of their service do not have a crisp intent in mind of zero-shot learning in RS. We develop an algorithm, dubbed ZEro-unlike in the retail shopping experience where the users often have Shot Recommenders (ZESRec), that is trained on an old dataset a crisp intent of purchasing something. The need for personalized and generalize to a new one where there are neither overlapping recommendations therefore arises from the fact that not only it is users nor overlapping items, a setting that contrasts typical crossdomain impractical to show all the items in the catalogue but often times RS that has either overlapping users or items. Different users of such services need help discovering the next best thing from categorical item indices, i.e., item ID, in previous methods, -- be it the new and exciting movie or be it a new music album or ZESRec uses items' natural-language descriptions (or description even a piece of merchandise that they may want to consider for embeddings) as their continuous indices, and therefore naturally future buying if not immediately.

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