Extensive evaluations demonstrate that AMT yields significant improvements, up to 12.92% in

Meta-learning has shown its effectiveness in adapting to new tasks with transferring the prior experience learned from other related tasks [7,34,38]. At ahigh level, the meta-learning process involves two steps: meta-training and meta-testing.

Extensive evaluations demonstrate that AMT yields significant improvements, up to 12.92% in

In addition, new knowledge is further incorporated into the selected blocks.

Recent advances in model pre-training give rise to task adaptation-based few-shot learning (FSL), where the goal is to adapt a pre-trained task-agnostic model for capturing task-specific knowledge with a few-labeled support samples of the target task.Nevertheless, existing approaches may still fail in the open world due to the inevitable in-distribution (ID) and out-of-distribution (OOD) noise from both support and query samples of the target task. With limited support samples available, i) the adverse effect of the dual noises can be severely amplified during task adaptation, and ii) the adapted model can produce unreliable predictions on query samples in the presence of the dual noises. In this work, we propose DEnoised Task Adaptation (DETA++) for reliable FSL. DETA++ uses a Contrastive Relevance Aggregation (CoRA) module to calculate image and region weights for support samples, based on which a clean prototype loss and a noise entropy maximization loss are proposed to achieve noise-robust task adaptation. Additionally,DETA++ employs a memory bank to store and refine clean regions for each inner-task class, based on which a Local Nearest Centroid Classifier (LocalNCC) is devised to yield noise-robust predictions on query samples. Moreover, DETA++ utilizes an Intra-class Region Swapping (IntraSwap) strategy to rectify ID class prototypes during task adaptation, enhancing the model's robustness to the dual noises. Extensive experiments demonstrate the effectiveness and flexibility of DETA++.

I did not understand the experimental setup for the sinusoid experiment in Section 5.2 (lines 238-241). The 10 disjoint intervals do not cover the whole domain [-5, 5]. What happens if x lies in (-4.5, -4) for example? Line 241: "there exists a continuous function that covers each piecewise component."; An illustration of this task (possibly in the Appendix) would make things clearer.

Neural Architecture Search (NAS) automates neural network design, reducing dependence on human expertise. While NAS methods are computationally intensive and dataset-specific, auxiliary predictors reduce the models needing training, decreasing search time. This strategy is used to generate architectures satisfying multiple computational constraints. Recently, Transferable NAS has emerged, generalizing the search process from dataset-dependent to task-dependent. In this field, DiffusionNAG is a state-of-the-art method. This diffusion-based approach streamlines computation, generating architectures optimized for accuracy on unseen datasets without further adaptation. However, by focusing solely on accuracy, DiffusionNAG overlooks other crucial objectives like model complexity, computational efficiency, and inference latency -- factors essential for deploying models in resource-constrained environments. This paper introduces the Pareto-Optimal Many-Objective Neural Architecture Generator (POMONAG), extending DiffusionNAG via a many-objective diffusion process. POMONAG simultaneously considers accuracy, number of parameters, multiply-accumulate operations (MACs), and inference latency. It integrates Performance Predictor models to estimate these metrics and guide diffusion gradients. POMONAG's optimization is enhanced by expanding its training Meta-Dataset, applying Pareto Front Filtering, and refining embeddings for conditional generation. These enhancements enable POMONAG to generate Pareto-optimal architectures that outperform the previous state-of-the-art in performance and efficiency. Results were validated on two search spaces -- NASBench201 and MobileNetV3 -- and evaluated across 15 image classification datasets.