Parameter Prediction
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Benchmarks
CIFAR10
Most implemented
Diet Networks: Thin Parameters for Fat Genomics
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
WISE: Whitebox Image Stylization by Example-based Learning
OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Papers
FreqAdapt: Frequency-Adaptive Processing for RAW Object Detection
Existing object detection methods predominantly utilize sRGB inputs, which are compressed from RAW sensor data using Image Signal Processors (ISP) originally designed for visualization purposes. Compared to RGB images, R…
Parameter PredictionObject DetectionParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. …
Parameter PredictionGraph LearningLinear-Time Global Visual Modeling without Explicit Attention
Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quadratic computational complexity. In this …
Parameter PredictionInstance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particu…
Parameter PredictionDisentangle-then-Align: Non-Iterative Hybrid Multimodal Image Registration via Cross-Scale Feature Disentanglement
Multimodal image registration is a fundamental task and a prerequisite for downstream cross-modal analysis. Despite recent progress in shared feature extraction and multi-scale architectures, two key limitations remain. …
Parameter PredictionImage RegistrationBreaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which …
Parameter Prediction