“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.

Object Detection in 20 Years: A Survey

Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23,463 images and 192,472 instances, covering 20 object classes. The proposed DIOR dataset (1) is large-scale on the object categories, on the object instance number, and on the total image number; (2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; (3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and (4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research.

Object detection in optical remote sensing images: A survey and a new benchmark

Recent Advances in Deep Learning for Object Detection

Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Stacking and bagging are widely used ensemble learning approaches that make use of multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous classifiers while bagging constructs an ensemble of homogenous classifiers. There exist some applications where it is essential for learning algorithms to be adaptive towards the training data. We propose A-Stacking and A-Bagging; adaptive versions of stacking and bagging respectively that take into consideration the similarity inherently present in the dataset. One of the main motives of ensemble learning is to generate an ensemble of multiple “experts” that are weakly correlated. We achieve this by producing a set of disjoint experts where each expert is trained on a different subset of the dataset. We show the working mechanism of the proposed algorithms on spoof fingerprint detection. The proposed versions of these algorithms are adaptive as they conform to the features extracted from the live and spoof fingerprint images. From our experimental results, we establish that A-Stacking and A-Bagging give competitive results on both balanced and imbalanced datasets.

A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection

A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection

Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful content. Automatic hate speech detection on social media platforms is an essential task that has not been solved efficiently despite multiple attempts by various researchers. It is a challenging task that involves identifying hateful content from social media posts. These posts may reveal hate outrageously, or they may be subjective to the user or a community. Relying on manual inspection delays the process, and the hateful content may remain available online for a long time. The current state-of-the-art methods for tackling hate speech perform well when tested on the same dataset but fail miserably on cross-datasets. Therefore, we propose an ensemble learning-based adaptive model for automatic hate speech detection, improving the cross-dataset generalization. The proposed expert model for hate speech detection works towards overcoming the strong user-bias present in the available annotated datasets. We conduct our experiments under various experimental setups and demonstrate the proposed model’s efficacy on the latest issues such as COVID-19 and US presidential elections. In particular, the loss in performance observed under cross-dataset evaluation is the least among all the models. Also, while restricting the maximum number of tweets per user, we incur no drop in performance.

Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19

Incremental learning enables the learner to accommodate new knowledge without retraining the existing model. It is a challenging task which requires learning from new data as well as preserving the knowledge extracted from the previously accessed data. This challenge is known as the stability-plasticity dilemma. We propose AILearn, a generic model for incremental learning which overcomes the stability-plasticity dilemma by carefully integrating the ensemble of base classifiers trained on new data with the current ensemble without retraining the model from scratch using entire data. We demonstrate the efficacy of the proposed AILearn model on spoof fingerprint detection application. One of the significant challenges associated with spoof fingerprint detection is the performance drop on spoofs generated using new fabrication materials. AILearn is an adaptive incremental learning model which adapts to the features of the ``live'' and ``spoof'' fingerprint images and efficiently recognizes the new spoof fingerprints as well as the known spoof fingerprints when the new data is available. To the best of our knowledge, AILearn is the first attempt in incremental learning algorithms that adapts to the properties of data for generating a diverse ensemble of base classifiers. From the experiments conducted on standard high-dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015, we show that the performance gain on new fake materials is significantly high. On an average, we achieve $49.57\%$ improvement in accuracy between the consecutive learning phases.

Shivang Agarwal

Papers

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection

A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection

Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19

AILearn: An Adaptive Incremental Learning Model for Spoof Fingerprint Detection.