https://github.com/jeffreyzpan/adversarial-playground
공식 코드On Intrinsic Dataset Properties for Adversarial Machine 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/jeongwhanchoi/gfsa
공식 코드Graph Convolutions Enrich the Self-Attention in Transformers!
https://github.com/jeroenbertels/deepvoxnet2
공식 코드https://github.com/jerry907/aaai24-rashb
공식 코드https://github.com/jessemelpolio/anytimecl
공식 코드Anytime Continual Learning for Open Vocabulary Classification
https://github.com/jessemelpolio/treeprobe
공식 코드Continual Learning in Open-vocabulary Classification with Complementary Memory Systems
https://github.com/jeunsom/TGD
공식 코드Leveraging Topological Guidance for Improved Knowledge Distillation
https://github.com/jfc43/informative-outlier-mining
공식 코드ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining
https://github.com/jfc43/robust-ood-detection
공식 코드https://github.com/jhcho99/coformer
공식 코드Collaborative Transformers for Grounded Situation Recognition
https://github.com/jhcho99/gsrtr
공식 코드https://github.com/jhkim89/PyramidNet
공식 코드https://github.com/jhnnsjkbk/aiitl
공식 코드Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
https://github.com/jhonatanoliveira/dcspns
공식 코드https://github.com/jhoon-oh/fedbabu
공식 코드FedBABU: Towards Enhanced Representation for Federated Image Classification
https://github.com/jhoon-oh/kd_data
공식 코드Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
https://github.com/jhornauer/heatmap_ood
공식 코드https://github.com/jiabi/ghostshiftaddnet
공식 코드GhostShiftAddNet: More Features from Energy-Efficient Operations
https://github.com/jiahao000/mfm
공식 코드Masked Frequency Modeling for Self-Supervised Visual Pre-Training
https://github.com/jiahongz/mlsom
공식 코드Extracting the Brain-like Representation by an Improved Self-Organizing Map for Image Classification
https://github.com/jiakangyuan/helixformer
공식 코드https://github.com/jiamingzhang94/imagenet-pretraining-transfers-non-robustness
공식 코드https://github.com/jianf-wang/np-match
공식 코드NP-Match: When Neural Processes meet Semi-Supervised Learning
https://github.com/jianf-wang/np-semiseg
공식 코드NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
https://github.com/jiangbo-shi/vila-mil
공식 코드ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
https://github.com/jianghaojun/CondenseNetV2
공식 코드CondenseNet V2: Sparse Feature Reactivation for Deep Networks
https://github.com/jiangsongtao/med-moe
공식 코드Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
https://github.com/jiangsongtao/tinymed
공식 코드Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
https://github.com/jiangtaoxie/MPN-COV
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/jiangtaoxie/fast-MPN-COV
공식 코드https://github.com/jiangwenj02/dynamic_loss
공식 코드https://github.com/jianhanlim/ipr-imagecaptioning
공식 코드Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection
https://github.com/jianhongbai/colt
공식 코드On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
https://github.com/jianlong-yuan/SimpleBaseline
공식 코드A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
https://github.com/jiansfoggy/CODE-SHOW/tree/master/Python/XnODR_and_XnIDR
공식 코드XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks
https://github.com/jiarunliu/co-correcting
공식 코드Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.