https://github.com/ubc-tea/dne-foundation-model-fairness
공식 코드Debiased Noise Editing on Foundation Models for Fair Medical 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/ubc-tea/fcro-fair-classification-orthogonal-representation
공식 코드https://github.com/ubc-tea/fedsoup
공식 코드https://github.com/uber-research/coordconv
공식 코드An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/uber-research/permute-quantize-finetune
공식 코드Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
https://github.com/uber/ludwig
공식 코드https://github.com/ucbdrive/dla
공식 코드https://github.com/ucbdrive/tafe-net
공식 코드TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
https://github.com/ucdvision/low-budget-al
공식 코드https://github.com/ucdvision/pranc
공식 코드https://github.com/uchidalab/book-dataset
공식 코드https://github.com/uchidalab/softmaxgradient-lrp
공식 코드Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation
https://github.com/uchiyama33/aosa
공식 코드Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
https://github.com/ucsc-real/canlessbemore
공식 코드Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
https://github.com/ucsc-real/cifar-10-100n
공식 코드Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/ucsc-real/negative-label-smoothing
공식 코드https://github.com/ucsc-real/selfsup_noisylabel
공식 코드Mitigating Memorization of Noisy Labels via Regularization between Representations
https://github.com/uditarora/ood-text-emnlp
공식 코드https://github.com/uds-lsv/bert-lnl
공식 코드Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
https://github.com/uestc-nnlab/mvol
공식 코드Mining In-distribution Attributes in Outliers for Out-of-distribution Detection
https://github.com/uitrbn/tscsi_idn
공식 코드https://github.com/uiuctml/HypStructure
공식 코드Learning Structured Representations with Hyperbolic Embeddings
https://github.com/uizard-technologies/realmix
공식 코드RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
https://github.com/ukaukaaaa/GazeGNN
공식 코드GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
https://github.com/ukplab/arxiv2025-ham-classifier
공식 코드An Efficient Quantum Classifier Based on Hamiltonian Representations
https://github.com/ultratykis/guiding-dnns-attention
공식 코드Efficient Human-in-the-loop System for Guiding DNNs Attention
https://github.com/um-dsp/PRICURE
공식 코드PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
https://github.com/um-dsp/morphence
공식 코드Morphence: Moving Target Defense Against Adversarial Examples
https://github.com/umair-nasir14/llmatic
공식 코드LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
https://github.com/umarkhalidai/cefhri-efficient-federated-learning
공식 코드https://github.com/umd-huang-lab/SWIFT
공식 코드SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
https://github.com/umd-huang-lab/perceptionclip
공식 코드PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
https://github.com/unites-lab/mew
공식 코드Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network
https://github.com/unites-lab/vpns
공식 코드Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
https://github.com/uo231492/dsilzsr
공식 코드https://github.com/uobinxiao/semantic_cross_attention_fsl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.