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Found 6 papers, 4 papers with code
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure.
Uncovering the Hidden Cost of Model Compression
This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.
Simulated Annealing in Early Layers Leads to Better Generalization
Our principal innovation in this work is to use Simulated annealing in EArly Layers (SEAL) of the network in place of re-initialization of later layers.
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning.
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deformation stability.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
