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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/bzhanguru/paper_class-m
공식 코드https://github.com/ca-joe-yang/discard-in-subsampling
공식 코드Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time
https://github.com/cai-jianfeng/HCLNet
공식 코드Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification
https://github.com/caidao22/pnode
공식 코드A memory-efficient neural ODE framework based on high-level adjoint differentiation
https://github.com/cair/An-Optimized-Toolbox-for-Advanced-Image-Processing-with-Tsetlin-Machine-Composites
공식 코드An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites
https://github.com/cair/TsetlinMachine
공식 코드https://github.com/cair/convolutional-tsetlin-machine
공식 코드https://github.com/cair/fast-tsetlin-machine-with-mnist-demo
공식 코드https://github.com/cair/plug-and-play-collaboration-between-specialized-tsetlin-machines
공식 코드TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines
https://github.com/cair/pyTsetlinMachine
공식 코드The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
https://github.com/cair/pyTsetlinMachineParallel
공식 코드https://github.com/cairo-thws/dbvce_eval
공식 코드Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation
https://github.com/caixdlab/cl4capro
공식 코드Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values
https://github.com/calvinmccarter/itlumm
공식 코드Look-ups are not (yet) all you need for deep learning inference
https://github.com/cambridge-mlg/DUN
공식 코드https://github.com/cambridge-mlg/LITE
공식 코드https://github.com/cambridge-mlg/cnaps
공식 코드Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
https://github.com/cambridge-mlg/dp-few-shot
공식 코드On the Efficacy of Differentially Private Few-shot Image Classification
https://github.com/cambridge-mlg/fit
공식 코드https://github.com/camcastera/INNA-for-DeepLearning
공식 코드https://github.com/camcastera/Indian-for-DeepLearning
공식 코드https://github.com/cammarasana123/weightedconvolution2.0
공식 코드Optimal Weighted Convolution for Classification and Denosing
https://github.com/cantordust/pycortex
공식 코드https://github.com/caodoanh2001/FALFormer
공식 코드FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
https://github.com/cap-lab/S3NAS
공식 코드S3NAS: Fast NPU-aware Neural Architecture Search Methodology
https://github.com/capsar/ts-inverse
공식 코드TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
https://github.com/capybaralet/REx_code_release
공식 코드Out-of-Distribution Generalization via Risk Extrapolation (REx)
https://github.com/cardwizard/membershipInferenceLottery
공식 코드https://github.com/carlosh93/ColorMAE
공식 코드ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoders
https://github.com/casia-iva-lab/pass-reid
공식 코드PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification
https://github.com/catalpaaa/demansia
공식 코드https://github.com/cbfinn/maml
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/cbfinn/maml_rl
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ccxu-ustc/sam
공식 코드https://github.com/cecilia-xue/agt
공식 코드Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.