https://github.com/salesforce/must
공식 코드Masked Unsupervised Self-training for Label-free Image Classification
Papers with Code 아카이브
Image Classification
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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/salesforce/online_conformal
공식 코드Improved Online Conformal Prediction via Strongly Adaptive Online Learning
https://github.com/salesforceairesearch/gemfilter
공식 코드https://github.com/salimkhazem/polygonet
공식 코드PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
https://github.com/samaonline/AIC_dualloss
공식 코드Successive Embedding and Classification Loss for Aerial Image Classification
https://github.com/samaonline/Orthogonal-Convolutional-Neural-Networks
공식 코드https://github.com/samaonline/Recurrent-Parameter-Generators
공식 코드Compact and Optimal Deep Learning with Recurrent Parameter Generators
https://github.com/samd770/generative-models-knowledge
공식 코드https://github.com/samirak98/fourierimaging
공식 코드Resolution-Invariant Image Classification based on Fourier Neural Operators
https://github.com/samsunglabs/sympie
공식 코드https://github.com/samwilso/safe_official
공식 코드SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
https://github.com/samzare/PICASO
공식 코드PICASO: Permutation-Invariant Cascaded Attentional Set Operator
https://github.com/sandialabs/packet2vec
공식 코드Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data
https://github.com/sangamesh-kodge/class_forgetting
공식 코드Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting
https://github.com/sanghyun-hong/deepsloth
공식 코드A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
https://github.com/sangnekim/smpconv
공식 코드SMPConv: Self-moving Point Representations for Continuous Convolution
https://github.com/sanketvmehta/lifelong-learning-pretraining-and-sam
공식 코드An Empirical Investigation of the Role of Pre-training in Lifelong Learning
https://github.com/sanketx/al-foundation-models
공식 코드Revisiting Active Learning in the Era of Vision Foundation Models
https://github.com/sanofi-public/dds-roformermil
공식 코드RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification
https://github.com/sarahesl/alignclip
공식 코드Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP
https://github.com/sarahpratt/cupl
공식 코드What does a platypus look like? Generating customized prompts for zero-shot image classification
https://github.com/sarapohland/parce
공식 코드https://github.com/sarthaxxxxx/palm
공식 코드PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation
https://github.com/sarthmit/BRIMs
공식 코드https://github.com/satchelfrench/ned
공식 코드Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification
https://github.com/sato9hara/PertMap
공식 코드Maximizing Invariant Data Perturbation with Stochastic Optimization
https://github.com/satunheim/convcotm-fpga-28x28
공식 코드An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
https://github.com/satunheim/convcotm_inference_accelerator
공식 코드An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
https://github.com/saurabhsharma1993/prototype-classifier-ltr
공식 코드https://github.com/sauradip/stale
공식 코드Zero-Shot Temporal Action Detection via Vision-Language Prompting
https://github.com/saurav-31/vitol
공식 코드ViTOL: Vision Transformer for Weakly Supervised Object Localization
https://github.com/savadikarc/gift
공식 코드https://github.com/sayakpaul/robustness-vit
공식 코드https://github.com/sbarratt/interpnet
공식 코드InterpNET: Neural Introspection for Interpretable Deep Learning
https://github.com/sbasu276/GBCNet
공식 코드Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
https://github.com/sbasu276/Weakly_DETR
공식 코드Gall Bladder Cancer Detection from US Images with Only Image Level Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.