Multi-Modal Fusion by Meta-Initialization

When experience is scarce, models may have insufficient information to adapt to a new task. In this case, auxiliary information - such as a textual description of the task - can enable improved task inference and adaptation. In this work, we propose an extension to the Model-Agnostic Meta-Learning algorithm (MAML), which allows the model to adapt using auxiliary information as well as task experience. Our method, Fusion by Meta-Initialization (FuMI), conditions the model initialization on auxiliary information using a hypernetwork, rather than learning a single, task-agnostic initialization. Furthermore, motivated by the shortcomings of existing multi-modal few-shot learning benchmarks, we constructed iNat-Anim - a large-scale image classification dataset with succinct and visually pertinent textual class descriptions. On iNat-Anim, FuMI significantly outperforms uni-modal baselines such as MAML in the few-shot regime. The code for this project and a dataset exploration tool for iNat-Anim are publicly available at https://github.com/s-a-malik/multi-few .Comment: The first two authors contributed equall

MetaViewer: Towards A Unified Multi-View Representation

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in features potentially degrade the quality of the derived representation. To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner. Specifically, we train a meta-learner, namely MetaViewer, to learn fusion and model the view-shared meta representation in outer-level optimization. Start with this meta representation, view-specific base-learners are then required to rapidly reconstruct the corresponding view in inner-level. MetaViewer eventually updates by observing reconstruction processes from uniform to specific over all views, and learns an optimal fusion scheme that separates and filters out view-private information. Extensive experimental results in downstream tasks such as classification and clustering demonstrate the effectiveness of our method.Comment: 8 pages, 5 figures, conferenc

Awesome-META+: Meta-Learning Research and Learning Platform

Artificial intelligence technology has already had a profound impact in various fields such as economy, industry, and education, but still limited. Meta-learning, also known as "learning to learn", provides an opportunity for general artificial intelligence, which can break through the current AI bottleneck. However, meta learning started late and there are fewer projects compare with CV, NLP etc. Each deployment requires a lot of experience to configure the environment, debug code or even rewrite, and the frameworks are isolated. Moreover, there are currently few platforms that focus exclusively on meta-learning, or provide learning materials for novices, for which the threshold is relatively high. Based on this, Awesome-META+, a meta-learning framework integration and learning platform is proposed to solve the above problems and provide a complete and reliable meta-learning framework application and learning platform. The project aims to promote the development of meta-learning and the expansion of the community, including but not limited to the following functions: 1) Complete and reliable meta-learning framework, which can adapt to multi-field tasks such as target detection, image classification, and reinforcement learning. 2) Convenient and simple model deployment scheme which provide convenient meta-learning transfer methods and usage methods to lower the threshold of meta-learning and improve efficiency. 3) Comprehensive researches for learning. 4) Objective and credible performance analysis and thinking

Contextualizing Multiple Tasks via Learning to Decompose

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts. Those ambiguities increase the difficulty of learning a generalizable model when there exists one concept or mixed concepts in a task. We propose a general approach Learning to Decompose Network (LeadNet) for both two cases, which contextualizes a model through meta-learning multiple maps for concepts discovery -- the representations of instances are decomposed and adapted conditioned on the contexts. Through taking a holistic view over multiple latent components over instances in a sampled pseudo task, LeadNet learns to automatically select the right concept via incorporating those rich semantics inside and between objects. LeadNet demonstrates its superiority in various applications, including exploring multiple views of confusing tasks, out-of-distribution recognition, and few-shot image classification

Entity Aware Modelling: A Survey

Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification, fairness, and knowledge-guided machine learning, can improve entity-aware modeling.Comment: Submitted to IJCAI, Survey Trac