https://github.com/s-sd/task-amenability
공식 코드Image quality assessment for machine learning tasks using meta-reinforcement learning
Papers with Code 아카이브
Image Classification
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- 평가표
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s1m0n38/semantic-encodings
공식 코드Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
https://github.com/sacmehta/EdgeNets
공식 코드ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/sacmehta/HATNet
공식 코드HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
https://github.com/sadegh28/atr
공식 코드Multi-Label Feature Selection Using Adaptive and Transformed Relevance
https://github.com/saehyung-lee/dcc
공식 코드https://github.com/safe-graph/graph-adversarial-learning-literature
공식 코드https://github.com/safednn-group/nap-ood
공식 코드Detection of out-of-distribution samples using binary neuron activation patterns
https://github.com/safezza/IQA-CNN-Adversarial-Attacks
공식 코드Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification
https://github.com/sagebei/Exploring-Parity-Challenges-in-Reinforcement-Learning
공식 코드Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
https://github.com/sagieppel/Classification-of-object-in-a-specific-image-region-using-a-convolutional-neural-net-with-ROI-mask-a
공식 코드Classifying a specific image region using convolutional nets with an ROI mask as input
https://github.com/sagieppel/Classification-of-the-material-given-region-of-an-image-using-a-convolutional-neural-net-with-attent
공식 코드Classifying a specific image region using convolutional nets with an ROI mask as input
https://github.com/sagizty/cellmix
공식 코드https://github.com/sagizty/mil-si
공식 코드Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification
https://github.com/sahagobinda/GPM
공식 코드https://github.com/sahagobinda/epr
공식 코드Saliency Guided Experience Packing for Replay in Continual Learning
https://github.com/sahagobinda/sgp
공식 코드https://github.com/sahilkhose/FloodNet
공식 코드Semi-Supervised Classification and Segmentation on High Resolution Aerial Images
https://github.com/saiboxx/chexray-ortho
공식 코드Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
https://github.com/saifhitawala/ResNeXt
공식 코드Evaluating ResNeXt Model Architecture for Image Classification
https://github.com/saikrishna-prathapaneni/LowDINO
공식 코드https://github.com/sail-sg/iformer
공식 코드https://github.com/sail-sg/inceptionnext
공식 코드https://github.com/sail-sg/metaformer
공식 코드https://github.com/sail-sg/mugs
공식 코드https://github.com/sail-sg/poolformer
공식 코드https://github.com/sail-sg/tec
공식 코드https://github.com/sail-sg/volo
공식 코드https://github.com/sainathadapa/urban-sound-tagging
공식 코드https://github.com/sajjad-sh33/medical-transfer-learning
공식 코드Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
https://github.com/sakshiudeshi/Neo
공식 코드Model Agnostic Defence against Backdoor Attacks in Machine Learning
https://github.com/salehmomeni/klda
공식 코드Continual Learning Using a Kernel-Based Method Over Foundation Models
https://github.com/salesforce/CoMatch
공식 코드CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
https://github.com/salesforce/MoPro
공식 코드https://github.com/salesforce/PCL
공식 코드Prototypical Contrastive Learning of Unsupervised Representations
https://github.com/salesforce/awd-lstm-lm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.