https://github.com/sbasu276/focusmae
공식 코드FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/sbasu276/radformer
공식 코드https://github.com/sbelharbi/deep-wsl-histo-min-max-uncertainty
공식 코드https://github.com/sbelharbi/negev
공식 코드Negative Evidence Matters in Interpretable Histology Image Classification
https://github.com/scabini/RADAM
공식 코드RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
https://github.com/scabini/parewiring_weights
공식 코드Improving Deep Neural Network Random Initialization Through Neuronal Rewiring
https://github.com/schmitt-muc/SEN12MS
공식 코드Remote Sensing Image Classification with the SEN12MS Dataset
https://github.com/sciml-fl/bra-fl
공식 코드https://github.com/scongl/dirmixe
공식 코드Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
https://github.com/scott-yjyang/diffmic
공식 코드DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
https://github.com/scottgeng00/unmet-promise
공식 코드The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
https://github.com/scu-sen/recursion-cellular-image-classification-challenge
공식 코드https://github.com/scuwyh2000/diff-ii
공식 코드Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification
https://github.com/sdc17/upop
공식 코드UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
https://github.com/sdemyanov/ConvNet
공식 코드Invariant backpropagation: how to train a transformation-invariant neural network
https://github.com/sdoerrich97/rethinking-model-prototyping-medmnistplus
공식 코드Rethinking model prototyping through the MedMNIST+ dataset collection
https://github.com/sds-lab/rot-pooling
공식 코드Regularized Optimal Transport Layers for Generalized Global Pooling Operations
https://github.com/sds-lab/sliceformer
공식 코드Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks
https://github.com/seai-lab/pygbs
공식 코드TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
https://github.com/seai-lab/torchspatial
공식 코드TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
https://github.com/sebgao/LIP
공식 코드https://github.com/seed-vt/tracefl
공식 코드TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
https://github.com/seijin-kobayashi/late-phase-weights
공식 코드https://github.com/selflein/EBM-OOD-Detection
공식 코드https://github.com/sense-x/mixmim
공식 코드MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
https://github.com/sense-x/uninet
공식 코드UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
https://github.com/sensilab/creativediscoveryvae
공식 코드https://github.com/senyuan-juncheng/TensorProjectionLayer
공식 코드TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks
https://github.com/seongmin-kye/MCT
공식 코드https://github.com/separius/dilemma
공식 코드Representation Learning by Detecting Incorrect Location Embeddings
https://github.com/sergiooramas/tartarus
공식 코드Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features
https://github.com/sergiormpereira/rr_segse
공식 코드https://github.com/sergulaydore/Feature-Grouping-Regularizer
공식 코드Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
https://github.com/sesmae/zoc
공식 코드Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
https://github.com/sethnabarro/gbp_learning
공식 코드Learning in Deep Factor Graphs with Gaussian Belief Propagation
https://github.com/seuzjj/DSK
공식 코드Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.