poster-2018.md

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page	CS231n Poster Session 2018	/poster-session-2018/

• Location: Jen-Hsun Huang Engineering Center
• Parking: Parking information can be found here
• Sponsor Setup Time: 11:15am - 12:00pm
• Poster Session: 12:00pm - 3:15pm
• Award Ceremony: 3:15pm - 3:30pm

The 2018 Stanford CS231N poster session will showcase projects in Convolutional Neural Networks for Visual Recognition that students have worked on over the past quarter. This year, 650 students will be presenting over 300 projects. The topics range from Generative Adversarial Networks (GANs), healthcare and medical imaging, art and style transfer, satellite imaging, self-driving cars, video understanding and more! See the list below for the projects that will be presented.

Catered food and refreshments will be made available over the course of the event.

This poster session has been made possible thanks to generous donations from Waymo, Bloomberg, Zoox, and NVIDIA!

List of Projects (3 digit number is ID to locate the poster)

• 101: Enhance! Image Super-Resolution
• 102: Super Resolution using CNNs and GANS
• 103: Modern Approaches for Real-Time Neural Style Transfer
• 104: A convolutional classification approach to colorization
• 105: Face swapping and harmonization using neural nets
• 106: Defendr
• 107: Automatic Graphic Generation from Text using GANs for Artists and Engineers
• 108: Generating Novel Images of Landmarks with WGANs
• 109: Generating Artwork with GANs
• 110: Audio2GIF
• 111: End-to-End Super Resolution Object Detection Networks
• 112: Conditional Face Generation with Deep Convolutional Generative Adversarial Networks
• 113: Learning a Meta-Discriminator for GANs
• 115: Exploring Adversarial Input Spaces for Convolutional Neural Network Defense
• 118: Contour-Aware Image-to-Image Translation
• 119: Human Portrait Colorization
• 120: Adversarial Image Segmentation
• 121: Artwork Classification and Style Transfer
• 122: Automatic Colorization through Transfer Learning
• 123: CNN models for classifying emotions in art images
• 124: Deep Learning for Image Completion
• 125: Computer Generated Art
• 127: Filter discriminators: adversarial loss for convolutional kernels
• 128: Anime Character Generation with GAN
• 129: Modifying the AttnGAN Discriminator for Improved Feature-Specific Text-to-Image Synthesis
• 130: Data Genie
• 131: Fully Unsupervised Aerial-to-Map Translation with Cycle-GAN
• 132: NeuralHash: An Adversarial Steganographic Method For Robust, Imperceptible Watermarking
• 133: Mapping Decision Boundaries of Convolutional Neural Network Classifiers
• 134: Distributionally Robust Adversarial Training
• 135: Generating realistic morphologies of neurons in rodent hippocampus with DGCAN
• 136: Text2Mesh using StackGAN
• 137: Detection and removal of biases using adversarial architectures
• 138: Cloud Removal in Hyperspectral Satellite Images using Generative Adversarial Networks
• 140: In your face! GAN Face Generation
• 141: Anonymity as a Service: Addressing Perceptual Biases by Anonymizing Facial Features
• 144: Global an Local Neural Style Transfer
• 146: End-to-End Neural Color Transfer of Subjects in Portrait Photography
• 147: Text to Artistic Image Generation with GANs
• 148: Be Creative: Style Transfer Mix & Match
• 149: Determine the Origin of Historical Artifacts
• 150: Style Transfer Incorporating Shape Deformability for Animation Genre Replication
• 200: Dog Breed Identification
• 203: Convolutional Neural Networks for Landmark Recognition
• 204: World Landmark Recognition
• 205: Distracted Driver Detection
• 206: Deep Shopping: Object Detection Based Fashion Recommendation
• 208: Capsule Networks on Modified Real-World Datasets
• 209: Aircraft Type Recognition with Convolutional Neural Networks
• 210: FloraDex: A Fine-Grained Flower Recognizer
• 212: Handing Noisy Bounding-Box Annotations with Faster R-CNN
• 213: A Convolutional Recurrent Attention Model for Fine-Grained Classification
• 215: Probabilistic Convolutional Neural Network for Probabilistic Inference
• 217: Optical Music Recognition based on Convolutional Neural Networks
• 218: Fine Grain Image Classification on Dog Breeds
• 219: TINE-CNN Augmentation: Automatic data augmentation for any image classification task
• 220: Fashion Product Recognition in Fine-Grained Visual Categorization
• 222: One-Shot Learning of Cosmetic Objects
• 223: Exploring Movie Poster Classification and Generation with Deep Convolutional Neural Networks
• 224: Classifying Electromagnetic Showers from Calorimeter Images with CNNs
• 228: Deep Stereo Fusion: Deep Learning for Disparity Map Estimation and Image Fusion with Dual Camera Phone Imagery
• 229: Automobile-Based Dual-Camera Object Segmentation
• 230: Fine-grained Classification of Furniture and Home Goods Images
• 231: Fine-grained Image Classification with Batch Contrastive Loss
• 232: Don't Judge a Movie by its Poster
• 233: Product multi-class classification using pretrained CNN models
• 234: SIFT++
• 237: Spatio-temporal large traffic network speed prediction using CNN
• 238: Shazam for Fashion
• 239: Composite Architecture for High Performance Image Classification
• 240: Traffic monitoring from seismic data
• 241: Image-based Merged Di-photon Identification for the ATLAS Experiment at the Large Hadron Collider
• 242: DeepClean: Deep Bayesian Restoration of Interferometric Images
• 243: 3D GAN Object Generation and Reconstruction
• 244: Image Segmentation for Autonomous Cars
• 245: Multi-Task Facial Landmark Detection with CNN
• 247: 3DNetView: Training Neural Networks to Perform 3D Image Reconstruction
• 248: Architecture for Automatic Fashion Product Labeling
• 249: Understanding Deforestation in the Amazon Basin with Neural Networks
• 250: Gotta Train 'Em All: Classifying Pokémon using Deep Learning
• 252: Movie Recommendation System Enhanced by Image Data and ConvNet
• 253: Biomedical Image Segmentation for Nuclei Detection
• 254: Self Attention Generative Adversarial Networks for High-Dimensional Scene Representations from Single 2D Images
• 255: Learning to Detect Light Field Features
• 300: Neural Techniques for Pose Guided Image Generation
• 301: Conversational Group Detection With Deep Convolutional Networks
• 302: Apparent Age Estimation From Facial Images
• 303: American Sign Language Gesture Detection
• 306: Human Body Pose Estimation
• 307: Learning to Feel: Training a CNN to recognize emotion
• 308: Monocular 3D Human Bounding Box Estimation
• 309: Decrypting human emotions
• 310: Semantic Segmentation(will update correct title)
• 311: Predicting Human Emotions from Images and Captions
• 312: Transformation Generalizability of Novel Objects in Human and Computational Vision
• 314: Classification of Dance Styles Using CNNs
• 316: Detecting Assaults in Surveillance Videos
• 317: Video Hand Gesture Recognition
• 319: Improving Affectnet: Emotion Classification
• 320: Video Gesture Classification Using Combined RGB and Depth Features
• 321: Predicting Hand Pose and Gesture from Monocular RGB Images
• 402: Medical Image Super-Resolution using GANs
• 403: 3D Reconstruction and Alignment of MRIs for Improved Medical Diagnostics
• 405: Learning electrode probing strategy in retinal prosthesis systems
• 408: PineappLeNet: PineappLeNet: Synthesizing Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Data
• 409: Classifying Dementia Ratings from MRI Imaging Data
• 413: Protein Functional Site Detection Using Amino Acid Contact Maps
• 415: Using Deep Learning for UIP Classification and Cyst Volume Calculation
• 417: BoneNet: Convolutional Methods for Abnormality Detection in the Lower Extremities
• 418: Does it look good? Evaluating GANs for Medical Imaging Applications
• 419: Detecting Unburnt Skin Using Deep Learning
• 423: Heirarchical Semantic Segmentation of Brain Tumors in MRIs using Convolutional Neural Networks
• 424: Cross-Institute Histopathology Image Stain Normalization with Deep Convolutional Neural Networks
• 426: Automated Burn Prognosis of Percent Total Body Surface Area
• 429: Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images
• 430: Segmentation of Stroke Lesion in T1-Weighted MRIs
• 431: Applying Deep Learning to Chest X-Rays for Pneumonia Detection
• 432: 3D Brain Tumor Segmentation
• 434: Thoracic Imaging Temporal Interpolation
• 437: Automated Iris Detection
• 438: Using Transfer Learning to classify Brain Tumors from Pathology Images
• 440: Preprocessing Histopathology Stains with Deep Learning
• 442: U-Net Architecture for Segmenting Nuclei in Medical Images
• 444: Diagnosing Retinal Pathology from Optical Coherence Tomography Using VGG19 and InceptionV3
• 445: Peak finding for crystallography
• 446: Reconstruction of multi-shot diffusion-weighted MRI using deep learning
• 450: Super-resolution MRIs with Semi-Supervised GANs
• 451: Classification and Segmentation of Brain Lesions after Stroke
• 452: Neural Networks for the Identification of Viable Eggs in In-vitro Fertilization
• 471: Dense Convolutional Networks for Abnormality Classification in Mammograms
• 455: 3D BrainNet: Brain Tumor Segmentation with Convolutional Neural Networks and Fully Connected CRFs
• 456: Lung Nodule Candidate Generation and Cancer Prediction
• 457: Anonymizing MRI Images via U-Net Image Segmentation
• 458: Tackling Diabetic Retinopathy with Visual Recognition
• 460: Unpaired Magnetic Resonance Image-to-Image Translation for Prostate using Cycle-Consistent Adversarial Networks
• 461: Rethinking Radiology: An Analysis of Different Approaches to BraTS
• 462: Classification Techniques for White Blood Cell Types
• 470: Segmentation of Stroke Lesions in T1-Weighted Brain MRIs using Deep Learning
• 500: D4QN: Distributed Deep Reinforcement Learning in the Arcade Learning Environment
• 501: Single Shot Robotic Grasp Pose Detection
• 502: Occupancy Grid Mapping for Robust and Efficient Learning
• 504: Robotic Grasping Planning using Convolutional Neural Networks
• 508: RevNet: An End-to-End Model for training self-driving simulated vehicles
• 509: LiDAR & RGB Fusion for 3D Bounding Box Estimation
• 510: Autonomous Driving Video Segmentation with Deep Learning
• 511: Recent Tesla Model X Autopilot Accident Analysis and Possible Solution via Traffic Sign and Lan Detection
• 512: Smaller is Better: Image Compression using Deep Learning
• 513: Multi-Task Learning in Visual Attribute Recognition
• 514: A look at the topology of convolutional neural networks
• 517: Deep Learning on LIDAR Point Cloud for 3D Object Detection
• 518: Semantic Segmentation in the Traffic Environment
• 519: Suction Affordance Maps for Grasping of Diverse Objects
• 521: Comparing Deep Neuroevolution to RL algorithms on Atari and Mujoco Environments
• 524: Using Human Gameplay to Augment Deep Q-Networks for Crypt of the NecroDancer
• 525: Comparing Learning Algorithms with Pac-Man
• 526: Twist to Grasp: Rotational Regional Proposals for Grasp Detection
• 529: Learning to Convolve
• 530: Adversarial Examples for YOLO Object Detection
• 531: High Fidelity Image Compression Using CNNs
• 533: Video Object Segmentation for Autonomous Vehicles
• 534: Object Detection in Autonomous Driving
• 535: Improving 3D Data Resolution: Using CNNs to Upscale Resolution of LIDAR Data
• 536: Semantic Segmentation on Autonomous Vehicle Data
• 537: Traffic Sign Detection and Classification using Capsule Network
• 539: Using t-SNE to Visualize Network Dynamics
• 540: Quantization Methodology for Efficient CNN Inference
• 600: Identifying Modes of Fishing from Ship Images
• 601: Enhancing Climate Data Resolution using Residual Networks
• 602: Detection of Cryogenic tanks
• 604: Tents Density Mapping of Refugee Camp via High-Resolution Remote Sensing Imagery
• 607: Convolutional Neural Networks for Radargram Segmentation
• 609: Power Plant Type Classification and Numerical Prediction Using Satellite Imagery Data
• 612: Semi-Supervised Image Segmentation for Satellite Imagery
• 700: DeepGIFs: Using Deep Learning to Understand and Synthesize Motion
• 701: Conventional Video Object Segmentation using Mask R-CNN and Online Learning
• 702: Moments in Time Challenge: Spatiotemporal information extraction from sequential video inputs using joint CNN-LSTM framework
• 703: Automated Visual Weak Supervision for Object Recognition in Videos
• 704: Using CNNs to Identify Player Actions in Basketball Videos
• 705: Video Compression with 3D CNNs
• 706: Action Recognition in the Moments In Time Dataset
• 707: Convolutional Neural Networks for MLB Pitch Recognition
• 711: Video Classification on the YouTube-8M Dataset
• 712: A Multi-Faceted Approach to Video Action Classification
• 713: Basketball Shot Quality Assessment with Deep Learning
• 714: Moments in Time: Deep Action Recognition
• 800: Spotting and Transcribing Structured Nutrition Information from Product Images
• 802: A Novel Approach using Weak Supervision to Label Digital Screenshots\ for Behavioral Analysis with Transfer Learning
• 803: End-To-End Trainable Model for Text Recognition
• 804: Converting Handwriting to Latex
• 807: Layerwise Quantization for Neural Networks
• 808: Image Retrieval and Image-Text Feature Alignment
• 809: Emoji-Language Image Captioning
• 810: Optimizing Reddit Posts
• 811: Culinary Images' Feature-Extraction And Recipe Generation Using Deep Convolutional Neural Network
• 812: Automatic Code Generation from UI Screenshots
• 813: GUCCI GAN: Grouped Unique Contextually-Classified Inpainting GAN
• 814: Adversarial Visual Question Answering
• 816: Handwritten Mathematics to LaTeX Code
• 817: Image captioning with attention
• 818: Identifying and Solving CAPTCHAs with Deep Learning
• 819: Mobile-Focused Networks for Classification of Chinese Text in the Wild
• 820: End-to-end Dense Video Captioning with Reinforcement Learning
• 823: Image Captioning using Policy Gradient Method
• 825: Chinese Character Synthesization From Components
• 826: Photo Quality Assessment with Deep CNNs
• 827: Bridging 3D shape and natural language
• 828: Neural Image Captioning on MSCOCO Dataset