https://github.com/bchidest/RiCNN
공식 코드Rotation Equivariance and Invariance in Convolutional Neural Networks
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/bcmi/Awesome-Weak-Shot-Learning
공식 코드Weak Novel Categories without Tears: A Survey on Weak-Shot Learning
https://github.com/bcmi/CaGNetv2-Zero-Shot-Semantic-Segmentation
공식 코드From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation
https://github.com/bcsaldias/yes-no-crowdsourcing
공식 코드A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
https://github.com/bd622/DiscretHashing
공식 코드Learning Binary Semantic Embedding for Histology Image Classification and Retrieval
https://github.com/beandkay/epass
공식 코드https://github.com/beandkay/sequencematch
공식 코드SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
https://github.com/bearcleverproud/hag-mil
공식 코드https://github.com/bearcleverproud/takt
공식 코드TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
https://github.com/behaapyy/dual-pruning
공식 코드Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
https://github.com/belimmer/PerturbationSaliencyEvaluation
공식 코드Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents
https://github.com/benathi/fastswa-semi-sup
공식 코드There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
https://github.com/benbergner/cropr
공식 코드https://github.com/benbergner/emil
공식 코드https://github.com/benbo/wsgan-paper
공식 코드https://github.com/benchopt/benchopt
공식 코드Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/bencrulis/binary_nn_extended
공식 코드https://github.com/benruben87/Learning-Curves-for-Heterogeneous-Feature-Subsampled-Ridge-Ensembles
공식 코드Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles
https://github.com/bentherien/ParametricScatteringNetworks
공식 코드https://github.com/benyeoh/grownup
공식 코드GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training
https://github.com/berleon/do_users_benefit_from_interpretable_vision
공식 코드Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
https://github.com/berndprach/aol
공식 코드Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
https://github.com/berng/LeNetImproving
공식 코드https://github.com/berryweinst/mms-select
공식 코드Selective sampling for accelerating training of deep neural networks
https://github.com/bethgelab/model-vs-human
공식 코드Partial success in closing the gap between human and machine vision
https://github.com/bethgelab/robustness
공식 코드Improving robustness against common corruptions by covariate shift adaptation
https://github.com/bfshi/toast
공식 코드https://github.com/bgu-cs-vil/deepdpm
공식 코드https://github.com/bgu-cs-vil/ditac
공식 코드https://github.com/bgu-cs-vil/wtconv
공식 코드https://github.com/bgucompsci/multigridinchannelscnns
공식 코드https://github.com/bhanML/Co-teaching
공식 코드Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
https://github.com/bhanML/Masking
공식 코드https://github.com/bhattarailab/OCT-Federated-Learning
공식 코드https://github.com/bhattarailab/ncdd
공식 코드NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision
https://github.com/bhavinc/predify2021
공식 코드Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.