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/rhero12/Slic
공식 코드https://github.com/riadibadulla/simulator
공식 코드FatNet: High Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks
https://github.com/richard-peng-xia/LMPT
공식 코드https://github.com/richardaecn/class-balanced-loss
공식 코드https://github.com/richardtml/divita
공식 코드https://github.com/richzhang/colorization
공식 코드https://github.com/richzhang/splitbrainauto
공식 코드Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
https://github.com/rickywrq/Progressive-Neural-Compression
공식 코드Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints
https://github.com/ridgerchu/TCJA
공식 코드TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks
https://github.com/riiswa/universal
공식 코드Universal Adversarial Perturbations: Efficiency on a small image dataset
https://github.com/rishabbala/contrastive_learning_for_object_detection
공식 코드https://github.com/rishgoyal/FXPQuantCNN
공식 코드https://github.com/rist-ro/training-neural-connectivities
공식 코드https://github.com/rizalzaf/AP-examples
공식 코드AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
https://github.com/rizalzaf/AdversarialPrediction.jl
공식 코드AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
https://github.com/rjbruin/flexconv
공식 코드FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
https://github.com/rjbruin/position-shap
공식 코드Learning to Adapt to Position Bias in Vision Transformer Classifiers
https://github.com/rl-vig/libfewshot
공식 코드https://github.com/robbie-xu/cpsd
공식 코드Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
https://github.com/robertcsordas/linear_layer_as_attention
공식 코드https://github.com/robertdvdk/lico-fact
공식 코드Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"
https://github.com/robin-chan/meta-ood
공식 코드https://github.com/rock-z/instilling-inductiva-bias
공식 코드https://github.com/rodriguesGabriel/energize
공식 코드https://github.com/rohban-lab/atd
공식 코드https://github.com/rohdelab/PyTransKit
공식 코드Invariance encoding in sliced-Wasserstein space for image classification with limited training data
https://github.com/rohdelab/cell-image-classification
공식 코드https://github.com/rohdelab/mathematical_augmentation
공식 코드Invariance encoding in sliced-Wasserstein space for image classification with limited training data
https://github.com/rohdelab/rcdt_ns_classifier
공식 코드Radon cumulative distribution transform subspace modeling for image classification
https://github.com/rois-codh/kaokore
공식 코드KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
https://github.com/rois-codh/kmnist
공식 코드https://github.com/romanpogodin/plausible-kernelized-bottleneck
공식 코드https://github.com/rongfei-chen/VCAN
공식 코드Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
https://github.com/ronmckay/uqgan
공식 코드https://github.com/rooa/eve
공식 코드Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.