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The process of editing an image can be naturally modeled as evaluating a counterfactual query: "What would an image look like if a particular feature had changed?" While recent advances in text-guided image editing leverage powerful pre-trained models to produce visually appealing images, they often lack counterfactual consistency - ignoring how features are causally related and how changing one may affect others. In contrast, existing causal-based editing approaches offer solid theoretical foundations and perform well in specific settings, but remain limited in scalability and often rely on labeled data. In this work, we aim to bridge the gap between causal editing and large-scale text-to-image generation through two main contributions. First, we introduce Backdoor Disentangled Causal Latent Space (BD-CLS), a new class of latent spaces that allows for the encoding of causal inductive biases. One desirable property of this latent space is that, even under weak supervision, it can be shown to exhibit counterfactual consistency. Second, and building on this result, we develop BD-CLS-Edit, an algorithm capable of learning a BD-CLS from a (non-causal) pre-trained Stable Diffusion model. This enables counterfactual image editing without retraining. Our method ensures that edits respect the causal relationships among features, even when some features are unlabeled or unprompted and the original latent space is oblivious to the environment's underlying cause-and-effect relationships.

In this section, we provide and expand upon a toy example. Recall that the inputs x and x0 need not correspond to real users but could instead represent hypothetical users. Example 5. Suppose that the regulatory guideline requires that users in the same geographical location receive similar weather forecasts. This can be written as "the weather forecasts that are selected by F should be similar for all users in the same geographical location", and S could be a randomly generated set of user pairs, where each pair corresponds to two (hypothetical) users in the same geographical location, and S could contain pairs across many locations. In the left-most panel, a filtering algorithm F takes in counterfactual inputs x and x0 and produces the content Z and Z0. Because a counterfactual regulation requires that F behave similarly under x and x0, the regulation is effectively requiring that content Z and Z0 are sufficiently similar (or, graphically, that they are close in Z). The question of how to quantify "similarity" is addressed in Section 2.1. The toy example in Example 5 is illustrated in the right-most panel.

The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that generate independent samples from complex molecular distributions, eliminating the cost of sampling rare transition events. However, a sampling problem remains when computing observables that rely on states which are rare in equilibrium, for example folding free energies. Here, we introduce enhanced diffusion sampling, enabling efficient exploration of rare-event regions while preserving unbiased thermodynamic estimators. The key idea is to perform quantitatively accurate steering protocols to generate biased ensembles and subsequently recover equilibrium statistics via exact reweighting. We instantiate our framework in three algorithms: UmbrellaDiff (umbrella sampling with diffusion models), $Δ$G-Diff (free-energy differences via tilted ensembles), and MetaDiff (a batchwise analogue for metadynamics). Across toy systems, protein folding landscapes and folding free energies, our methods achieve fast, accurate, and scalable estimation of equilibrium properties within GPU-minutes to hours per system -- closing the rare-event sampling gap that remained after the advent of diffusion-model equilibrium samplers.

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

Inventor John Tse has gone high-tech to keep raindrops from falling on your head. Breakthroughs, discoveries, and DIY tips sent six days a week. You wouldn't think it, but for years people have looked at the humble umbrella and seen more than just a way to keep dry during a rainstorm. They see it as a challenge. Are there ways to use it we never thought of before?

The advent of multimodal deep learning models, such as CLIP, has unlocked new frontiers in a wide range of applications, from image-text understanding to classification tasks. However, these models are not safe for adversarial attacks, particularly backdoor attacks, which can subtly manipulate model behavior. Moreover, existing defense methods typically involve training from scratch or fine-tuning using a large dataset without pinpointing the specific labels that are affected. In this study, we introduce an innovative strategy to enhance the robustness of multimodal contrastive learning models against such attacks. In particular, given a poisoned CLIP model, our approach can identify the backdoor trigger and pinpoint the victim samples and labels in an efficient manner. To that end, an image segmentation ``oracle'' is introduced as the supervisor for the output of the poisoned CLIP. We develop two algorithms to rectify the poisoned model: (1) differentiating between CLIP and Oracle's knowledge to identify potential triggers; (2) pinpointing affected labels and victim samples, and curating a compact fine-tuning dataset. With this knowledge, we are allowed to rectify the poisoned CLIP model to negate backdoor effects. Extensive experiments on visual recognition benchmarks demonstrate our strategy is effective in CLIP-based backdoor defense.

The'Simprella' weighs less than a few Snickers' bars. Breakthroughs, discoveries, and DIY tips sent every weekday. If you're anything like this author, you've probably burned through your fair share of umbrellas over the years. Large, cumbersome, and often prone to breaking in the wind, umbrellas are one of those necessities of life that, more often than not, end up creating a bigger annoyance than the problem they're meant to solve. A startup called Simp Design is trying to do just that with its new iPhone-sized " Simprella ."