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Found 14 papers, 10 papers with code
Joint or Disjoint: Mixing Training Regimes for Early-Exit Models
Early exits are an important efficiency mechanism integrated into deep neural networks that allows for the termination of the network's forward pass before processing through all its layers.
Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference
The computational cost of transformer models makes them inefficient in low-latency or low-power applications.
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion
Transformer models can face practical limitations due to their high computational requirements.
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age.
Computer Vision based inspection on post-earthquake with UAV synthetic dataset
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods.
SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier
The proposed classifier is confident only if a single class has a high probability and other probabilities are negligible.
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting.
Hard hat wearing detection based on head keypoint localization
In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems, especially regarding personal protective equipment.
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network.
KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance
Monitoring the technical condition of infrastructure is a crucial element to its maintenance.
Finding the Optimal Network Depth in Classification Tasks
We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads.
LOSSGRAD: automatic learning rate in gradient descent
In this paper, we propose a simple, fast and easy to implement algorithm LOSSGRAD (locally optimal step-size in gradient descent), which automatically modifies the step-size in gradient descent during neural networks training.
