1차 : 리뷰를 마친 논문 목록 및 리뷰 예정 논문 목록

리뷰 마친 논문 목록

A. design neural networks and object detection

1. Going deeper with convolutions

2. Rethinking the Inception Architecture for Computer Vision

3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

4. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

5. Deep Residual Learning for Image Recognition

6. Residual Networks are Exponential Ensembles of Relatively Shallow Networks

7. Speed/accuracy trade-offs for modern convolutional object detectors

8. Dropout: A simple way to prevent neural networks from overfitting

//Dropout은 언급만하고 요지만 설명후 넘어감

2차 정기모임까지 리뷰 예정 논문 목록

B. Viusal semantic embedding

1. Deep Visual-Semantic Alignments for Generating Image Descriptions

2. Order-embeddings of images and language

3. Unifying visual-semantic embeddings with multimodal neural language models

4. Multimodal convolutional neural networks for matching image and sentence

C. Image caption

1. Show and Tell: a neural image caption generator

2. Show, Adapt and Tell: Adversarial training of cross-domain image captioner

3. Show, Attend and Tell: Neural image caption generation with visual attention

4. Bottom-up and top-down attention for image captioning and visual question answering

D. unsupervised learning, Deep Generative Model

1. Building high-level features using large scale unsupervised learning

2. Auto-encoding variational bayes

3. Generative adversarial nets

4. Unsupervised representation learning with deep convolutional generative adversarial networks

5. DRAW: A recurrent neural network for image generation.

6. Pixel recurrent neural networks

7. Conditional image generation with PixelCNN decoders