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Found 8 papers, 2 papers with code
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments.
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data.
Live American Sign Language Letter Classification with Convolutional Neural Networks
This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed.
SimHaze: game engine simulated data for real-world dehazing
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
An optimization method for out-of-distribution anomaly detection models
Frequent false alarms impede the promotion of unsupervised anomaly detection algorithms in industrial applications.
Self-supervised Video Representation Learning with Cascade Positive Retrieval
Implementation-wise, CPR is complementary to pretext tasks and can be easily applied to previous work.
Efficient Inference of CNNs via Channel Pruning
The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in compressing and accelerating trained CNN models.
Design Rule Violation Hotspot Prediction Based on Neural Network Ensembles
Design rule check is a critical step in the physical design of integrated circuits to ensure manufacturability.
