[pr12] pr-026: notes for cvpr machine learning sessions
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Notes for CVPR 2017: Machine Learning Sessions
Paper reviewed by Taegyun Jeon
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Paper TableSpotlight
1-1AExclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaobo Wang et al.
Borrowing Treasures From the Wealthy: Deep Transfer Learning Through Selective Joint Fine-Tuning
Weifeng Ge, Yizhou Yu
The More You Know: Using Knowledge Graphs for Image Classification Kenneth Marino et al.
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Martin Simonovsky, Nikos Komodakis
Convolutional Neural Network Architecture for Geometric Matching Ignacio Rocco et al.
Deep Affordance-Grounded Sensorimotor Object Recognition Spyridon Thermos et al.
Discovering Causal Signals in Images David Lopez-Paz et al.
On Compressing Deep Models by Low Rank and Sparse Decomposition Xiyu Yu et al.
Oral 1-1A PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Q et al.Universal Adversarial Perturbations Seyed-Mohsen Moosavi-Dezfooli et al.
Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks Konstantinos Bousmalis et al.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig et al.
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Borrowing Treasures From the Weealthy0904 Borrowing Treasures From the Wealthy: Deep Transfer Learning Through Selective Joint Fine-Tuning
Key Idea: deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data.
● Shallow feature space: Gabor filters (48) + 1st and 2nd convolutional layers of AlexNet (ImageNet)
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The More You KnowThe More You Know: Using Knowledge Graphs for Image Classification
Key Idea: structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves per- formance on image classification
(Visual Genome Graph and WordNet)
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On Compressing Deep Models by Low Rank and Sparse Decomposition0928 On Compressing Deep Models by Low Rank and Sparse Decomposition
Key idea: unified framework integrating the low-rank and sparse decomposition of weight matrices with the feature map reconstructions
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Booth InformationNVIDIA
● NVIDIA DGX-1 Station 소개○ 가격 ~$69,000. (학교나 연구소 할인 프로모션 있음)○ Volta 아키텍쳐 Tesla P100 x 4장 포함. (지금 DGX-1을 구매하면 pascal 아키텍쳐로 판매 이후 Volta로 업그레이드)○ 9월경 출시 (변경가능)○ 구매 대수에 따라 NVIDIA Cloud 플랫폼 사용권 제공
● NVIDIA Cloud○ TensorFlow, CNTK, PyTorch, Caffe등 대부분의 모든 딥러닝 라이브러리를 NVIDIA Docker상에 제공.○ 스케쥴링 기능 추가○ NVIDIA DIGITS과 UI를 계승. 상당부분 개선.
● NVIDIA Jetson 보드 소개● 학회중 Best Paper Award받은 학생들에게 젠슨황이 직접와서 GPU뿌리고 감.● 학회에서 진행된 워크샵의 competition 입상 선물들이 대부분 NVIDIA Titan XP였음. (이번 학회의 5개 워크샵 및 튜토리얼 후원)● NVIDIA Inception program: 스타트업들에게 플랫폼을 제공, GTC 행사에서 발표기회 제공, GPU Ventures의 투자대상 포함● Caffe2 Meetup 행사 운영
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APPLE
● 질문: MachineLearning blog 최근에 개설했는데 어떤 방향으로 진행할거냐고 물어봄○ 대답: 계속해서 사람들을 모으고 있고 애플 제품들을 위한 서비스에 개발 (두리뭉실)
● 질문: 작년에 GAN논문 하나 내고 그뒤로 별로 paper work이 없다. 연구는 하고 있는거냐?○ 대답: 비밀리에 하고 있다. 회사에서 내부적으로만 연구중이다.
● 지난번 NIPS와 마찬가지로 별다른 데모도 없고, 아이페드만 깔아놓고 리쿠르팅만 운영
Amazon
● Alexa, Echo등을 내새운 IoT시장을 장악하기 위한 초기 진입장벽을 허물고 있는중.● Amazon GO등 새로운 아이템들 폭풍 선전● Amazon A9: 아마존 온라인 플랫폼에서 상품 추천을 위해 사용되는 자체 기술. 계속 좋아지는중. 자랑자랑.
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Uber
● Uber ATG, Uber Mapping에 이어 토론토에 Uber AI Lab 최근 신설● Uber delivery, Uber X등에 사용되는 알고리즘 개발에 치중
INTEL
● Movidius Neural Compute Stick 런칭○ 소형 플랫폼을 타겟으로 USB에 딥러닝 모델을 업로드하여 소규모 장비에도 적용 가능 ($79). ○ 불티나게 팔림.
● 리쿠르팅 : 회사 자체가 설명이 필요없는 존재. 우리 짱임. 무조건 와라. 이런 분위기.
● 리쿠르팅 : 회사 자체가 설명이 필요없는 존재. 우리 짱임. 무조건 와라. 이런 분위기.