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Found 5 papers, 1 papers with code
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods.
Ranked #1 on
Image Compression
on BSDS500
Exploring Mathematical Extrapolation of Large Language Models with Synthetic Data
In this paper, through a newly proposed arithmetical puzzle problem, we show that the model can perform well on multi-step reasoning tasks via fine-tuning on high-quality synthetic data.
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
Finally, using the progressive training (P), the features extracted by the model in different stages can be fully utilized and fused with each other.
Self-supervised Models are Good Teaching Assistants for Vision Transformers
These findings motivate us to introduce an self-supervised teaching assistant (SSTA) besides the commonly used supervised teacher to improve the performance of transformers.
Learning Class Unique Features in Fine-Grained Visual Classification
A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details.
Ranked #14 on
Fine-Grained Image Classification
on FGVC Aircraft
