https://github.com/j-min/dalleval
공식 코드DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
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/j-zarka/separation_concentration_deepnets
공식 코드https://github.com/j201/keras-ste-layers
공식 코드Regularizing Neural Networks by Stochastically Training Layer Ensembles
https://github.com/j4ckliu/sacp
공식 코드Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification
https://github.com/jacenkow/mmbt
공식 코드https://github.com/jack24658735/fedlgt
공식 코드Language-Guided Transformer for Federated Multi-Label Classification
https://github.com/jackbrocp/entaugment
공식 코드EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification
https://github.com/jackhck/madaug
공식 코드When to Learn What: Model-Adaptive Data Augmentation Curriculum
https://github.com/jackyfl/wico
공식 코드Window Token Concatenation for Efficient Visual Large Language Models
https://github.com/jacobandreas/l3
공식 코드https://github.com/jacobgil/pytorch-grad-cam
공식 코드KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA
https://github.com/jaehakim97/sr4ir
공식 코드Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
https://github.com/jaeho-lee/layer-adaptive-sparsity
공식 코드https://github.com/jaeill/CVPR23-VNE
공식 코드VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
https://github.com/jahuerta92/cnn-prob-ensemble
공식 코드Fusing CNNs and statistical indicators to improve image classification
https://github.com/jaisidhsingh/con-clip
공식 코드Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations
https://github.com/jakesnell/prototypical-networks
공식 코드https://github.com/james128333/HarDNet-MSEG
공식 코드https://github.com/janegerken/sem_seg_s2cnn
공식 코드https://github.com/jasonustc/caffe-multigpu
공식 코드Transform-Invariant Convolutional Neural Networks for Image Classification and Search
https://github.com/javadzb/class-balanced-al
공식 코드https://github.com/javierorjuela/medical-interpretability-polyp-detection
공식 코드Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging
https://github.com/jayanthkoushik/alphanet
공식 코드AlphaNet: Improving Long-Tail Classification By Combining Classifiers
https://github.com/jayaram-r/adversarial-detection
공식 코드A General Framework For Detecting Anomalous Inputs to DNN Classifiers
https://github.com/jayelm/compexp
공식 코드https://github.com/jayendrakantipudi/Color-Channel-Perturbation-Attack
공식 코드https://github.com/jayjaynandy/RBF-CNN
공식 코드Approximate Manifold Defense Against Multiple Adversarial Perturbations
https://github.com/jayjaynandy/maximize-representation-gap
공식 코드Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
https://github.com/jayrothenberger/meta-co-training
공식 코드https://github.com/jayroxis/dam-pytorch
공식 코드Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
https://github.com/jcperdomo/robust_attacks
공식 코드https://github.com/jcruan519/idat
공식 코드https://github.com/jcruan519/petrobustness
공식 코드Understanding Robustness of Parameter-Efficient Tuning for Image Classification
https://github.com/jdonnelly36/Deformable-ProtoPNet
공식 코드Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
https://github.com/jeff024/palm
공식 코드Learning with Mixture of Prototypes for Out-of-Distribution Detection
https://github.com/jeffhernandez1995/vic-mae
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.