https://github.com/IlyaDobrynin/GridMixup
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Aggregated Residual Transformations for Deep Neural Networks
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
Bag of Tricks for Image Classification with Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
Aggregated Residual Transformations for Deep Neural Networks
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.