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Found 6 papers, 0 papers with code
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
In this approach, we do not assume test-time access to the labeled source dataset.
Unsupervised Information Obfuscation for Split Inference of Neural Networks
In our method, the edge device runs the model up to a split layer determined based on its computational capacity.
Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls for Network Pruning
We report, for the first time, on the cascade weight shedding phenomenon in deep neural networks where in response to pruning a small percentage of a network's weights, a large percentage of the remaining is shed over a few epochs during the ensuing fine-tuning phase.
Private Split Inference of Deep Networks
Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks.
Learned Threshold Pruning
This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process.
