However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. ![]() Finally, we share our thoughts on future research directions. ![]() To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Furthermore, we discuss some typical chemical applications supported by MRL. Then we summarize and categorize MRL methods into three groups based on their input. Specifically, we first introduce the features of 2D and 3D molecular graphs. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. With vision-free unsupervised training, Knight achieves state-of-the-art performance in zero-shot methods for image captioning and video captioning. In this paper, we propose the K-nearest-neighbor Cross-modality Mapping (Knight), a zero-shot method from association to generation. To solve the problem, we try to map images/videos to the language modality and generate captions from the language modality. Although previous works have created generation capacity for CLIP through additional language models, a modality gap between the CLIP representations of different modalities and the inability of CLIP to model the offset of this gap, which results in the failure of the concept to transfer across modes. This is due to the lack of decoder architecture and pre-training tasks for generation. However, CLIP is hard to apply to generation-based tasks. With the development of Vision-Language Pre-training Models (VLPMs) represented by CLIP and ALIGN, significant breakthroughs have been achieved for association-based visual tasks such as image classification and image-text retrieval by the zero-shot capability of CLIP without fine-tuning. Special Track on AI, the Arts and Creativity.Special Track on AI The Arts And Creativity PCs.Special Track on AI for Social Good PCs. ![]()
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