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Surgical fine-tuning improves adaptation to distribution shifts
A common approach to transfer learning under distribution shift is to fine-tune the last few
layers of a pre-trained model, preserving learned features while also adapting to the new …
layers of a pre-trained model, preserving learned features while also adapting to the new …
Source-free adaptation to measurement shift via bottom-up feature restoration
Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a
source domain to unlabelled data in a target domain without access to the source-domain …
source domain to unlabelled data in a target domain without access to the source-domain …
Layer-wise auto-weighting for non-stationary test-time adaptation
Given the inevitability of domain shifts during inference in real-world applications, test-time
adaptation (TTA) is essential for model adaptation after deployment. However, the real-world …
adaptation (TTA) is essential for model adaptation after deployment. However, the real-world …
Autoft: Robust fine-tuning by optimizing hyperparameters on ood data
Foundation models encode a rich representation that can be adapted to a desired task by
fine-tuning on task-specific data. However, fine-tuning a model on one particular data …
fine-tuning on task-specific data. However, fine-tuning a model on one particular data …
Dynamic fine‐tuning layer selection using Kullback–Leibler divergence
The selection of layers in the transfer learning fine‐tuning process ensures a pre‐trained
model's accuracy and adaptation in a new target domain. However, the selection process is …
model's accuracy and adaptation in a new target domain. However, the selection process is …
Unit-level surprise in neural networks
To adapt to changes in real-world data distributions, neural networks must update their
parameters. We argue that unit-level surprise should be useful for:(i) determining which few …
parameters. We argue that unit-level surprise should be useful for:(i) determining which few …
Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices
The personalization of machine learning (ML) models to address data drift is a significant
challenge in the context of Internet of Things (IoT) applications. Presently, most approaches …
challenge in the context of Internet of Things (IoT) applications. Presently, most approaches …
ATTL: An automated targeted transfer learning with deep neural networks
Success of machine learning algorithms hinges on access to labeled dataset. Obtaining a
labeled dataset is an expensive, challenging and time-consuming process, leading to the …
labeled dataset is an expensive, challenging and time-consuming process, leading to the …
Improved transfer learning using textural features conflation and dynamically fine-tuned layers
Transfer learning involves using previously learnt knowledge of a model task in addressing
another task. However, this process works well when the tasks are closely related. It is …
another task. However, this process works well when the tasks are closely related. It is …
[HTML][HTML] A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology
Due to an insufficient amount of image annotation, artificial intelligence in computational
histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine …
histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine …