https://github.com/CtrlZ1/Domain-Adaptive-CodeBase
Learning Transferable Features with Deep Adaptation Networks
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.
Learning Transferable Features with Deep Adaptation Networks
Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Student-Teacher Feature Pyramid Matching for Anomaly Detection
An Empirical Study of Training Self-Supervised Vision Transformers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Learning Efficient Convolutional Networks through Network Slimming
Learning Efficient Convolutional Networks through Network Slimming
DropBlock: A regularization method for convolutional networks
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Unsupervised Representation Learning by Predicting Image Rotations
A Simple Framework for Contrastive Learning of Visual Representations
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.