Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments.

With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios.

We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores.

Figure 1: Overview of the Transformer block used in the PromptIR framework. As mentioned in section 3.1.2 Bias-free convolutions are utilized within this submodule. After MDT A Module the features are processed through the GDFN module. Our method effectively removes haze to produce visually better images.