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Practical Bayesian Optimization of Machine Learning Algorithms
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Practical Bayesian Optimization of Machine Learning Algorithms
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
AutoSpeech: Neural Architecture Search for Speaker Recognition
Unsupervised Representation Learning by Predicting Image Rotations
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Exploring Randomly Wired Neural Networks for Image Recognition
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation
Aggregated Residual Transformations for Deep Neural Networks
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
Exploring Randomly Wired Neural Networks for Image Recognition
SNIP: Single-shot Network Pruning based on Connection Sensitivity
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.