https://github.com/uoguelph-mlrg/Cutout
공식 코드Improved Regularization of Convolutional Neural Networks with Cutout
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/uppaal/lm-ood
공식 코드Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection
https://github.com/uriballo/qml-satellite-image-classification
공식 코드https://github.com/uristern123/united-we-stand-using-epoch-wise-agreement-of-ensembles-to-combat-overfit
공식 코드United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit
https://github.com/usagisukisuki/elmloss
공식 코드https://github.com/usc-sail/mica-MovieCLIP
공식 코드https://github.com/usuyama/ePillID-benchmark
공식 코드ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification
https://github.com/utrerf/robust_transfer_learning
공식 코드Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
https://github.com/uvavision/Curriculum-Labeling
공식 코드Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
https://github.com/uvavision/chair-segments
공식 코드Chair Segments: A Compact Benchmark for the Study of Object Segmentation
https://github.com/uw-hai/limeade
공식 코드https://github.com/uwm-bigdata/multi-modal-wound-classification-using-images-and-locations
공식 코드Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
https://github.com/uwm-bigdata/wound_classification
공식 코드Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier
https://github.com/uzh-rpg/svit
공식 코드Revisiting Token Pruning for Object Detection and Instance Segmentation
https://github.com/vaishnavmohit/Conviformer
공식 코드https://github.com/val-iisc/DeiT-LT
공식 코드DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
https://github.com/val-iisc/VL2V-ADiP
공식 코드Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
https://github.com/val-iisc/saddle-longtail
공식 코드Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
https://github.com/valentyn1boreiko/dvces
공식 코드https://github.com/valeoai/ConfidNet
공식 코드https://github.com/valeoai/genval
공식 코드Reliability in Semantic Segmentation: Can We Use Synthetic Data?
https://github.com/valeoai/obow
공식 코드OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
https://github.com/valeoai/obsnet
공식 코드https://github.com/vamosc/colearning-meet-stitchup
공식 코드Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition
https://github.com/vanderschuea/stthree
공식 코드Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?
https://github.com/vanint/core-tuning
공식 코드https://github.com/vanint/sade-agnosticlt
공식 코드Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
https://github.com/varun19299/rigl-reproducibility
공식 코드https://github.com/varunmannam/Papers_with_Code/tree/master/VAE_project
공식 코드Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
https://github.com/vaynexie/cwox
공식 코드Two-Stage Holistic and Contrastive Explanation of Image Classification
https://github.com/vcheplygina/cats-scans
공식 코드https://github.com/vdigpku/flownas
공식 코드FlowNAS: Neural Architecture Search for Optical Flow Estimation
https://github.com/vegarant/Invfool
공식 코드On instabilities of deep learning in image reconstruction - Does AI come at a cost?
https://github.com/vegarant/troublesome_kernel
공식 코드https://github.com/vemvet/rotation-meanout-network
공식 코드A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
https://github.com/vengdeng/fcro
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.