https://github.com/adrhill/explainableai.jl
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Self-training with Noisy Student improves ImageNet classification
A Simple Framework for Contrastive Learning of Visual Representations
Defending Against Unforeseen Failure Modes with Latent Adversarial Training
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Training data-efficient image transformers & distillation through attention
High-Performance Large-Scale Image Recognition Without Normalization
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.