https://github.com/toshi2k2/CompnetDA
공식 코드A Bayesian Approach to OOD Robustness 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/toshihiro-ota/imixer
공식 코드iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer
https://github.com/toummHus/HAIR
공식 코드https://github.com/tqwei05/PlantSeg
공식 코드PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
https://github.com/trais-lab/adversarial-attack-data-attribution
공식 코드https://github.com/trangptm/Column_networks
공식 코드https://github.com/transmuteai/data-efficient-transformers
공식 코드Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
https://github.com/tristangomez44/br_npa
공식 코드https://github.com/trungtrinh44/damp
공식 코드Improving robustness to corruptions with multiplicative weight perturbations
https://github.com/trungtrinh44/ibnn
공식 코드Scalable Bayesian neural networks by layer-wise input augmentation
https://github.com/tsachiblau/Class-Conditioned-Transformation-for-Enhanced-Robust-Image-Classification
공식 코드Class-Conditioned Transformation for Enhanced Robust Image Classification
https://github.com/tsborkar/DeepCorrect
공식 코드DeepCorrect: Correcting DNN models against Image Distortions
https://github.com/tsinghua-fib-lab/uv-sam
공식 코드UV-SAM: Adapting Segment Anything Model for Urban Village Identification
https://github.com/tsinghua-msiip/uni-med
공식 코드Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
https://github.com/tsumansapkota/Input-Invex-Neural-Network
공식 코드https://github.com/ttbrunner/biased_boundary_attack
공식 코드Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
https://github.com/ttbrunner/biased_boundary_attack_avc
공식 코드Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
https://github.com/tue-mps/algm-segmenter
공식 코드https://github.com/tue-mps/cts-segmenter
공식 코드Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers
https://github.com/tufts-ml/data-emphasized-ELBo
공식 코드https://github.com/tufts-ml/ssl-for-echocardiograms
공식 코드https://github.com/tufts-ml/ssl-vs-ssl-benchmark
공식 코드https://github.com/tuggeluk/pycls
공식 코드https://github.com/tum-vision/dca
공식 코드https://github.com/tum-vision/lgm
공식 코드Probabilistic Discriminative Learning with Layered Graphical Models
https://github.com/tungchintao/flowcut
공식 코드FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models
https://github.com/tuomasjalonen/rope-damage-sensors
공식 코드Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks
https://github.com/twke18/Adversarial_Structure_Matching
공식 코드Adversarial Structure Matching for Structured Prediction Tasks
https://github.com/twke18/cast
공식 코드Learning Hierarchical Image Segmentation For Recognition and By Recognition
https://github.com/tyler-hayes/Continual-Analogical-Reasoning
공식 코드Selective Replay Enhances Learning in Online Continual Analogical Reasoning
https://github.com/tyohei/chainerkfac
공식 코드Scalable and Practical Natural Gradient for Large-Scale Deep Learning
https://github.com/tyxe-bdl/tyxe
공식 코드https://github.com/tztztztztz/eql.detectron2
공식 코드https://github.com/ub-gold/ub-gold
공식 코드Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
https://github.com/ubc-systopia/adaptive-randomized-smoothing
공식 코드Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
https://github.com/ubc-tea/Improving-Fairness-in-Image-Classification-via-Sketching
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.