https://github.com/hchoi71/AMC-Loss
공식 코드AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in 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/hchoi71/intra-class-patch-swap
공식 코드https://github.com/hchoi71/mix-kd
공식 코드Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
https://github.com/hcmlab/GANterfactual
공식 코드https://github.com/hcplab-sysu/hcp-mlr-pl
공식 코드Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
https://github.com/hcyang-null/mild
공식 코드MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
https://github.com/he-y/filter-pruning-geometric-median
공식 코드Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
https://github.com/healthx-lab/biomedcoop
공식 코드BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
https://github.com/heaventian93/ImageNet-Models-on-Domain-Adaptation
공식 코드https://github.com/hedixia/heavyballnode
공식 코드https://github.com/heekhero/ACSR
공식 코드https://github.com/helioszhao/shade-visualdg
공식 코드Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
https://github.com/hellowangqian/UDA-norm-VAE
공식 코드Data Augmentation with norm-VAE for Unsupervised Domain Adaptation
https://github.com/hellowangqian/multi-label-image-classification
공식 코드https://github.com/hellowangqian/sganet-crowd-counting
공식 코드Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
https://github.com/hellozhuo/msgc
공식 코드Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution
https://github.com/hendrycks/outlier-exposure
공식 코드https://github.com/henggao12/oal
공식 코드https://github.com/henrikmarklund/arm
공식 코드Adaptive Risk Minimization: Learning to Adapt to Domain Shift
https://github.com/henriquejsb/spenser
공식 코드SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks
https://github.com/henulwy/apnt
공식 코드Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification
https://github.com/henulwy/stbdip
공식 코드Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
https://github.com/hewell0/projood
공식 코드https://github.com/hezhangsprinter/SROSR
공식 코드https://github.com/hfmei/idc
공식 코드Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness
https://github.com/hfp/libxsmm
공식 코드Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
https://github.com/hfut-mialab/slidegcd
공식 코드https://github.com/hfutmars/megcf
공식 코드MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
https://github.com/hhhhhhao/general-framework-weak-supervision
공식 코드https://github.com/hic-messaoudi/cross-dimensional-transfer-learning-in-medical-image-segmentation-with-deep-learning
공식 코드Cross-dimensional transfer learning in medical image segmentation with deep learning
https://github.com/hikvision-research/SparseShiftLayer
공식 코드https://github.com/hila-chefer/robustvit
공식 코드Optimizing Relevance Maps of Vision Transformers Improves Robustness
https://github.com/hirl-team/eurnet-image
공식 코드EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
https://github.com/hiroid/oodf
공식 코드https://github.com/hiroki11x/losslandscapegeometry
공식 코드No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
https://github.com/hitachi-rd-cv/CDB-loss
공식 코드Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.