We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation used to condition diffusion models from the perspective that ideal representations should improve sample fidelity, be easy to generate, and be compositional to allow out-of-training samples generation. We introduce Discrete Latent Code (DLC), an image representation derived from Simplicial Embeddings trained with a self-supervised learning objective. DLCs are sequences of discrete tokens, as opposed to the standard continuous image embeddings. They are easy to generate and their compositionality enables sampling of novel images beyond the training distribution. Diffusion models trained with DLCs have improved generation fidelity, establishing a new state-of-the-art for unconditional image generation on ImageNet. Additionally, we show that composing DLCs allows the image generator to produce out-of-distribution samples that coherently combine the semantics of images in diverse ways. Finally, we showcase how DLCs can enable text-to-image generation by leveraging large-scale pretrained language models. We efficiently finetune a text diffusion language model to generate DLCs that produce novel samples outside of the image generator training distribution.

We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their conditioning. This paper investigates the representation used to condition diffusion models from the perspective that ideal representations should improve modeling the data distribution, be easy to generate, and be compositional to allow generalizing outside the training distribution. We introduce Discrete Latent Code (DLC), an image representation derived from Simplicial Embeddings trained with a self-supervised learning objective. DLCs are sequences of discrete tokens, as opposed to the standard continuous image embeddings. They are easy to generate and their compositionality enables sampling of novel images beyond the training distribution. Diffusion models trained with DLCs improve generation fidelity, establishing a new state-of-the-art for unconditional image generation on ImageNet. Additionally, we show that composing DLCs allows the image generator to produce interesting out-of-distribution samples that coherently combine the semantics of images in diverse ways. Finally, we showcase how DLCs can enable text-to-image generation by leveraging large-scale pretrained language models. Using only 9M image-caption pairs, we efficiently finetune a text diffusion model to generate novel DLCs that produces samples outside of the data distribution used to train the image generator.

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Existing class-incremental learning (CIL) approaches based on replay or knowledge distillation are often constrained by forgetting or the stability-plasticity dilemma. Some expansion-based approaches could achieve higher accuracy. However, they always require significant parameter increases. In this paper, we propose a plugin extension paradigm termed the Deployment of extra LoRA Components (DLC) for non-pre-trained CIL scenarios.We treat the feature extractor trained through replay or distillation as a base model with rich knowledge. For each task, we use Low-Rank Adaptation (LoRA) to inject task-specific residuals into the base model's deep layers. During inference, representations with task-specific residuals are aggregated to produce classification predictions. To mitigate interference from non-target LoRA plugins, we introduce a lightweight weighting unit. This unit learns to assign importance scores to different LoRA-tuned representations. Like downloadable contents in software, our method serves as a plug-and-play enhancement that efficiently extends the base methods. Remarkably, on the large-scale ImageNet-100, with merely 4 % of the parameters of a standard ResNet-18, our DLC model achieves a significant 8 % improvement in accuracy, demonstrating exceptional efficiency. Moreover, it could surpass state-of-the-art methods under the fixed memory budget.

The graphical model of DGP is summarized in Figure 1 . First let's define the potential function Now let's define the Gaussian bump. We will write everything in vector form hereafter. We want to "let the data speak" and avoid oversmoothing, so the penalty weights Given the approximate posterior (eq. To understand the various terms in the ELBO above it is helpful to start with a simpler special case.

Cyberpunk 2077 developer CD Projekt Red has confirmed it used AI voice cloning software to reconstruct the voice of a deceased actor for its Phantom Liberty DLC. Actor Miłogost Reczek voiced the character Viktor Vektor in the Polish version of the game and would have been tapped to reprise the role for the DLC, which came out last month, but he died in 2021 before its production. The developer told Bloomberg it decided to go this route as a way to "pay tribute to his wonderful performance," and was given permission to do so by his family. Instead of replacing Reczek outright, CD Projekt Red worked with Respeecher, the Ukraine-based voice tech company known for deaging Mark Hamill's voice in The Mandalorian and The Book of Boba Fett to create a young Luke Skywalker. Another actor was hired to speak the new lines, and Respeecher's software reworked them into Reczek's voice, CD Projekt localization director Mikołaj Szwed told Bloomberg.

Harnessing collective intelligence to drive effective decision-making and collaboration benefits from the ability to detect and characterize heterogeneity in consensus beliefs. This is particularly true in domains such as technology acceptance or leadership perception, where a consensus defines an intersubjective truth, leading to the possibility of multiple "ground truths" when subsets of respondents sustain mutually incompatible consensuses. Cultural Consensus Theory (CCT) provides a statistical framework for detecting and characterizing these divergent consensus beliefs. However, it is unworkable in modern applications because it lacks the ability to generalize across even highly similar beliefs, is ineffective with sparse data, and can leverage neither external knowledge bases nor learned machine representations. Here, we overcome these limitations through Infinite Deep Latent Construct Cultural Consensus Theory (iDLC-CCT), a nonparametric Bayesian model that extends CCT with a latent construct that maps between pretrained deep neural network embeddings of entities and the consensus beliefs regarding those entities among one or more subsets of respondents. We validate the method across domains including perceptions of risk sources, food healthiness, leadership, first impressions, and humor. We find that iDLC-CCT better predicts the degree of consensus, generalizes well to out-of-sample entities, and is effective even with sparse data. To improve scalability, we introduce an efficient hard-clustering variant of the iDLC-CCT using an algorithm derived from a small-variance asymptotic analysis of the model. The iDLC-CCT, therefore, provides a workable computational foundation for harnessing collective intelligence under a lack of cultural consensus and may potentially form the basis of consensus-aware information technologies.

After consulting with clients to nail down what they want in their dream vacation home, you design both the interior and exterior just as you can with your home on the mainland. The surrounding landscape -- foliage, pathways, even the weather -- can also be modified to your liking. You can invite specific clients using amiibo cards. Players will help design vacant buildings on the archipelago as well, including a school, hospital and restaurant. Using the Switch's online features, you can show off your designs, check out those of other players and follow your favorite vacation home designers.

When Assassin's Creed Valhalla new expansion comes out this summer, it will allow players to relieve the Siege of Paris, Ubisoft announced at its Forward E3 event on Saturday. Historically, the 845 CE event culminated with the Vikings occupying the city and doing what they did best, plundering it for all it was worth. How the event will unfold in Valhalla, we'll see, but Ubisoft promised the DLC will include new weapons, gear and abilities for players to discover. Additionally, The Siege of Paris will see the return of a fan favorite feature: black box infiltration missions. Leaning into the franchise's sandbox roots, these will give you an objective to complete, but how you go about accomplishing it will be up to you.

Downloadable content is such a common part of modern gaming life it's rote. A few levels here, a few skins or characters there--DLC is pretty much a given, especially on AAA titles, which these days you can almost bet will have at least two paid bits of bonus content that encompass entirely new missions. And, hey, for a long time they made sense. Video game companies like them because they're sure-fire revenue generators; players (learned to) like them because they can extend time in a beloved game, a digital amuse-bouche of their favorite dish. But just because something makes sense, doesn't mean I have to play it.