AI Research Archive

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AI Research Archive는 인공지능(AI) 기술이 사회, 인간, 정책에 미치는 영향을 다학제적 관점에서 탐색하기 위한 디지털 아카이브입니다. 이 아카이브는 단순한 기술 성과를 넘어서, 윤리, 공정성, 안전성, 사회적 책임을 중심 주제로 연구 자료를 분류하여 제공합니다.

Collection 안내

AI Ethics & Social Impact – AI의 윤리, 법제도, 사회적 책임, 알고리즘 편향, 규제 이슈
Natural Language Processing – 언어 모델, 생성형 AI, 다국어 모델, 프롬프트 설계 등
Computer Vision – 이미지 분류, 의료 영상 분석, GAN, 비전 트랜스포머 등
Reinforcement Learning – 강화학습 알고리즘, 정책 최적화, 안전한 자율 시스템 등
AI in Practice – 의료, 교육, 환경 등 다양한 분야에서의 AI 적용 사례

News

업데이트 기록

2025-05-27 – AI 윤리 컬렉션 5편 탐색
2025-05-25 – NLP, CV 컬렉션 기본 구조 완성 및 논문 탐색
2025-05-20 – 커뮤니티 개설 및 Collection 구조 확정

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Now showing 1 - 4 of 4

Deep Residual Learning for Image Recognition
(arXiv, 2015-12-10) He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Dermatologist-level classification of skin cancer with deep neural networks
(Nature, 2017-01-25) Esteva, Andre; Kuprel, Brett; Novoa, Roberto A; Ko, Justin; Swetter, Susan M; Blau, Helen M; Thrun, Sebastian
Skin cancer, the most common human malignancy1,2,3, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6,7,8,9,10,11. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Fully Convolutional Networks for Semantic Segmentation
(arXiv, 2015-11-14) Long, Jonathan; Shelhamer, Evan; Darrell, Trevor
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.
Scaling Laws for Neural Language Models
(arXiv, 2020-01-23) Kaplan, Jared; McCandlish, Sam; Henighan, Tom; Brown, Tom B; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; Amodei, Dario
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

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Browsing AI Research Archive by Subject "Deep Learning"

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