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Found 9 papers, 4 papers with code
Simplicity Bias of Transformers to Learn Low Sensitivity Functions
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of the inductive biases that they have and how those biases are different from other neural network architectures remains elusive.
Implicit Bias and Fast Convergence Rates for Self-attention
Self-attention, the core mechanism of transformers, distinguishes them from traditional neural networks and drives their outstanding performance.
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness
Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer learning 'simple' features over more 'complex' ones, even when the latter may be more informative.
GradML: A Gradient-based Loss for Deep Metric Learning
Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning
Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.
Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling
Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e. g. $f$, $2f/3$) and across three frequencies (e. g. $f_1$, $f_2$, $f_1+f_2$), respectively.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction
Although state-of-the-art deep learning based methods have been able to obtain fast, high-quality reconstruction of CS-MR images, their main drawback is that they treat complex-valued MRI data as real-valued entities.
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks
Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit.
