AI Scholar Weekly: 6
By: Christopher Dossman
Tags: Deep Learning, Artificial Intelligence, Machine Learning, Programming, Data Science

AI Scholar Weekly: 6

Deep reinforcement learning for creating models that learn like humans, enhanced edge detection with DFF, dataset generator for vehicle to vehicle encounters, improving clinical concept extraction with contextual embedding, and more.

Pseudo-Rehearsal for Achieving Deep Reinforcement Learning Without Catastrophic Forgetting

The new model integrates pseudo-rehearsal, a deep generative model and a dual memory scheme to bring about an efficient method that requires no additional storage requirements even as the number of tasks increase. Through iterations, the model learns three Atari games and keeps above human-level performance on all three games while performing as efficiently as a set of networks that have been individually trained to perform the tasks.

All this is achieved without revisiting data from past tasks. Compared to existing algorithms on deep reinforcement tasks, the new model has shown that is doesn’t forget past tasks like conventional models.

Potential Uses and Effects

Researchers and the general AI community can leverage the new model to further advance deep reinforcement research efforts as well as apply the model for cutting edge video gaming, self-driving cars, and real-world robotics applications. Given a large enough network could this be the key to human-like intelligent agents?

Read more: https://arxiv.org/abs/1812.02464v2

Dual Attention Networks (DAN) for Improved Visual Reference Resolution

Recently, researchers laid out the groundwork to solve the problem of visual reference resolution by proposing for DAN for enhanced visual reference resolutions. DAN implements two types of attention networks including REFER and FIND. REFER is specially designed to learn the relationships between queries and a dialog history by implementing the self-attention approach.

Conversely, FIND takes image features and reference-aware representations input (REFER module output) and achieves visual grounding by implementing a bottom-up attention technique. Both quantitative and qualitative evaluation of DAN on VisDial v1.0 and v0.9 datasets shows that it outperforms existing visual dialog models by a huge margin.

Potential Uses and Effects

The AI community can implement DAN to realize visual reference resolutions for various visual dialog tasks including collaborative dialog systems. Because it doesn’t rely on the previous visual attention maps, DAN can be used to resolve unclear visuals by implementing REFER component, and ground resolved references for visual images using FIND model component.

Read more: https://arxiv.org/abs/1902.09368v1

Dynamic Feature Fusion (DFF) Method for Enhanced Edge Detection

Researchers from China have managed dynamic feature fusion by proposing a new dynamic feature fusion (DFF) strategy that allocates diverse fusion weights for diverse images and locations. DFF comprises of two modules including a feature extractor and an adaptive weight fusion component. The model achieves dynamic feature fusion by implementing a weight model to infer proper fusion weights over multi-level features for every single location in a feature map that is conditioned on a particular specific input.

After performing experiments on standard benchmark datasets including Cityscapes and SBD, DFF demonstrates that it can greatly improve model performance by locating more accurate edges on object margins as well as subduing insignificant edge responses in objects.

Potential Uses and Effects

Semantic Edge Detection aims at jointly extracting edges and their category info to enable high-end applications in domains including semantic segmentation, object recognition and more. The proposed DFF it is the first research work that is aimed at learning adaptive fusion weights conditioned on input data to merge multi-level features in SED research to facilitate and achieve state-of-the-art on the SED tasks. The location-adaptive weight learner can be improved by considering high-level and low-level backbone feature maps.

Read more: https://arxiv.org/abs/1902.09104v1

Framework for Offline and Online Corner Case Detection for Autonomous Driving

This new study defines corner case detection and proposes a framework that can handle video signals from front cameras of a moving vehicle and generate a corner case score for both online and the offline use cases. According to the researchers behind this system framework, the corner case detection system can be employed as a backup warning functionality for providing information about unusual scenes for autonomous driving systems. Additionally, with regard to an offline mode, the corner case detection framework can be used to analyze vast amounts of video data to return any unusual data.

The technical corner case detection framework is trained for both the segmentation and image prediction on Cityscapes dataset which contains a wide variety of street images from 50 cities.

Potential Uses and Effects

Autonomous vehicles researchers and engineers can implement corner case framework to develop more focused training for autonomous driving systems since it helps tackle the problem of underrepresented critical training data. The system can also be used to help select relevant scenarios for storing and (re)training AI models

Furthermore, the proposed corner case detection framework is effective for enabling enhanced developments in motion detection, image registry, video tracking, image mosaicking, 3D modeling, panorama stitching, object recognition and more.

Read more: https://arxiv.org/abs/1902.09184v1

Dataset Generator for Vehicle-to-Vehicle Encounters

A lack of enough training data has slowed advances in autonomous driving technology. However, this recently published simulator model now does away with this limitation by providing sufficient data and resources to help engineers implement effective autonomous vehicle development and testing.

The multi-vehicle trajectory generator (MTG) can encode multi-vehicle scenarios (driving encounters) into interpretable representations for generating new high-quality driving encounter. The generator model comprises of a bi-directional variational autoencoder and a multi-branch decoder to achieve its function.

The study also proposes a new disentanglement metric with the potential to comprehensively analyze created trajectories and model driving scenarios robustness. This new generator model truly demonstrates a stronger capability to create high-end driving scenarios when compared to existing VAE and infoGAN models.

Potential Uses and Effects

The multi-vehicle trajectory generator is a great step towards solving one of the issues with autonomous vehicles. Mainly a lack of varied and original data. It’s not just driverless cars that will get a boost. You could extend this method into other fields of Deep learning that have similar data problems.

Read more: https://arxiv.org/abs/1809.05680v5

HighResolution Net (HRNet) for High-resolution Human Pose Estimation

Unlike traditional approaches that connect subnetworks in a serial fashion, the new HRNet approach connects high-to-low resolution subnetworks in a parallel manner which make it possible to maintain high-end resolution, as well as achieve a superior and accurate key point prediction. Additionally, many existing fusion schemes combine low-level and high-level representations while HRNet performs repeated multi-scale fusions to enhance the high-resolution representations which are imperative for high-quality pose estimation.

HRNet has demonstrated its effectiveness over conventional methods after experiments over COCO keypoint detection and MPII Human Pose datasets. What more, HRNet has shown superiority in pose tracking after being tested on the PoseTrack dataset. All the models and code are publicly available on this link.

Potential Uses and Effects

Researchers and developers can apply HRNet for high-level object detection, activity recognition, semantic segmentation, human-computer interaction (HCI), virtual reality, augmented reality, face detection and alignment, image detection and classification, translation and any other application that relies on services that track and measure human activities such as Amazon Go. I’m looking forward to when my smartphone can tell me of my weight lifting form is ok.

Read more: https://arxiv.org/abs/1902.09212v1

Code: https://github.com/leoxiaobin/deep-high-resolution-net.pytorch

Deep Reinforcement Learning approach for Image Hashing (DRLIH)

DRLIH is the first research work to address image hashing challenges from a deep reinforcement learning perspective.

The proposed deep learning network comprises of a feature representation network and a policy network. The policy network utilizes a Recurrent Neural Network(RNN) that serves as the agent to chronologically project images into binary codes.

Network design helps generate and project images into hash code 1, and compute hash code 0 probabilities. The researchers also propose a sequential learning strategy which learns hashing functions by correcting the errors presented by previous functions to promote retrieval accuracy. DRLIH approach has been tested on three standard datasets and the results demonstrate its effectiveness over conventional image hashing methods.

Potential Uses and Effects

DRLIH technique arises as an effective tool to accurately represent, index, retrieve and automatically identify images. It can be used for more efficient image authentication by querying whether images are fabrications or copies of original ones. DRLIH can also be used for effective local storage or caching preventing photo retransmissions or storage duplicates as well as in applications that were previously accomplished by watermarking such as copyright protection.

Read more: https://arxiv.org/abs/1802.02904v2

Contextual Embedding to Improve Clinical Concept Extraction

New research proposes a new approach to deal with this standing challenge. Researchers evaluate a variety of embedding methods including word2vec, GloVe fastText, ELMo, and BERT. They also perform an analysis to cover four clinical concept corpora that demonstrates the generalizability of each of the above-mentioned techniques.

More importantly, they develop pre-trained contextualized embeddings using large clinical corpus and compare the performance with pre-trained models. They lastly demonstrate a detailed analysis on the effect of pre-training on clinical corpora compared to open domain corpora and report a performance increase for clinical concept extraction that achieves state-of-the-art results on all test corpora. The study results show the benefits of embeddings on clinical text corpora which achieves greater performance than conventional models across all tasks.

Potential Uses and Effects

For clinical concept extraction, contextual embedding has looming potential to greatly advance automated text processing. Additionally, it can enable researchers to have increased accessibility of clinical text which can further advance the field in terms of enhanced information management and unstructured clinical text data mining.

Read more: https://arxiv.org/abs/1902.08691v1