Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods. However, they require high performance computing systems (such as supercomputer clusters and GPU arrays) due to their highly complex and large computational architectures. Additionally, deep neural networks require machine learning experts to delicately design and fine-tune the large, complex architectures. This issue of complexity has increased greatly over time, driven by the demand for increasingly deeper and larger networks to boost cognitive accuracy. As such, it has become near impossible to take advantage of such powerful yet complex deep neural networks in scenarios where computational and energy resources are scarce, such as in embedded systems, as well as increasingly more difficult to hand-craft their architectures. Inspired by nature, the team at VIP lab have developed several pioneering strategies for enabling powerful yet operational deep intelligence by considering a radically different idea: Can deep neural networks evolve naturally over generations to become not only highly efficient but also powerful?
We have introduced the concept of evolutionary deep intelligence, where we evolve deep neural networks over multiple generations to become more efficient yet smart. The ‘DNA’ of each generation of deep neural networks is encoded computationally and used, along with simulated environmental factors such as those encouraging computational and energy efficiency through natural selection, to ‘give birth’ to its offspring deep neural networks, with the process repeating generation after generation. These ‘evolved’ offspring deep neural networks will naturally have more efficient, more varied architectures than their ancestor deep neural networks (due to natural selection and random mutations) while achieving powerful cognitive capabilities.
Experimental results from a study using the MSRA-B and HKU-IS datasets demonstrated that the synthesized offspring deep neural networks can achieve state-of-the-art F-beta scores while having network architectures that are significantly more efficient, with a staggering ~48X fewer synapses by the fourth generation compared to the original, first-generation ancestor network. This level of performance was further reinforced by experimental results from a study using the MNIST dataset, which demonstrated synthesized offspring deep neural networks can achieve state-of-the-art accuracy (>99%) while having network architectures that are significantly more efficient, with a staggering ~40X fewer synapses by the seventh generation compared to the original, first-generation ancestor network. More remarkably, an accuracy of ~98% was still achieved by thirteen-generation offspring deep neural networks with an incredible ~125X fewer synapses compared to the original, first-generation ancestor network.
The concept of evolutionary deep intelligence has won numerous awards, including a Best Paper Award at the NIPS Workshop on Efficient Methods for Deep Neural Networks, a Best Paper Award at the Conference on Computational Vision and Intelligence Systems, named by MIT Technology Review as one of the most interesting and thought-provoking papers on arXiv, and named on Reddit as one of the papers that demonstrate the beauty of deep learning.
Related people
Directors
Students
Alumni
Related publications
Aboutalebi H., Shafiee M.J., Tai C.-E.A., and Wong A, "Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Dive", IEEE Access, vol. 12, pp. 48174-48188, 2024. Get it here. Carrington A.M., Manuel D.G., Fieguth P.W., Ramsay T., Osmani V., Wernly B., Bennett C., Hawken S., Magwood O., Sheikh Y., Mcinnes M., and Holzinger A, "Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, pp. 329-341, 2023. Get it here. Chen W., Liu Y., Wang W., Bakker E.M., Georgiou T., Fieguth P., Liu L., and Lew M.S, "Deep Learning for Instance Retrieval: A Survey", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, pp. 7270-7292, 2023. Get it here. Song J., Ebadi A., Florea A., Xi P., Tremblay S., and Wong A, "COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound", Sensors, vol. 23, 2023. Get it here. Gomrokchi M., Amin S., Aboutalebi H., Wong A., and Precup D, "Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning", IEEE Access, vol. 11, pp. 42796-42808, 2023. Get it here. Zhu Z., Parker W., and Wong A, "Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging", Environmental Pollution, vol. 127, 2023. Get it here. Ma K., He S., Sinha G., Ebadi A., Florea A., Tremblay S., Wong A., and Xi P, "Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis", Sensors, vol. 23, 2023. Get it here. Fang Y., Xu L., Chen Y., Zhou W., Wong A., and Clausi D.A, "A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 4571-4582, 2022. Get it here. Kamyab S., Azimifar Z., Sabzi R., and Fieguth P, "Deep learning methods for inverse problems", PeerJ Computer Science, vol. 8, 2022. Get it here. Aboutalebi H., Pavlova M., Shafiee M.J., Sabri A., Alaref A., and Wong A, "COVID-net CXR-S: Deep convolutional neural network for severity assessment of COVID-19 cases from chest X-ray images", Diagnostics, vol. 12, 2022. Get it here. Laschowski B., McNally W., Wong A., and McPhee J, "Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks", Frontiers in Neurorobotics, vol. 15, 2022. Get it here. Aboutalebi H., Pavlova M., Gunraj H., Shafiee M.J., Sabri A., Alaref A., and Wong A, "MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis", Frontiers in Medicine, vol. 8, 2022. Get it here. Pavlova M., Terhljan N., Chung A.G., Zhao A., Surana S., Aboutalebi H., Gunraj H., Sabri A., Alaref A., and Wong A, "COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images", Frontiers in Medicine, vol. 9, 2022. Get it here. Lee J.R.H., Pavlova M., Famouri M., and Wong A, "Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images", BMC Medical Imaging, vol. 22, 2022. Get it here. Wong A., Lee J.R.H., Rahmat-Khah H., Sabri A., Alaref A., and Liu H, "TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray", Frontiers in Artificial Intelligence, vol. 5, 2022. Get it here. Gunraj H., Sabri A., Koff D., and Wong A, "COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learni", Frontiers in Medicine, vol. 8, 2022. Get it here. Shafiee M.J., Jeddi A., Nazemi A., Fieguth P., and Wong A, "Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement", IEEE Signal Processing Magazine, vol. 38, pp. 22-30, 2021. Get it here. Abdar M., Pourpanah F., Hussain S., Rezazadegan D., Liu L., Ghavamzadeh M., Fieguth P., Cao X., Khosravi A., Acharya U.R., Makarenkov V., and Nahavandi S, "A review of uncertainty quantification in deep learning: Techniques, applications and challenges", Information Fusion, vol. 76, pp. 243-297, 2021. Get it here. Wong A., Lin Z.Q., Wang L., Chung A.G., Shen B., Abbasi A., Hoshmand-Kochi M., and Duong T.Q, "Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 ches", Scientific Reports, vol. 11, 2021. Get it here. Ebadi A., Xi P., Tremblay S., Spencer B., Pall R., and Wong A, "Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing", Scientometrics, vol. 126, pp. 725-739, 2021. Get it here. Abbasi S., Famouri M., Shafiee M.J., and Wong A, "Outliernets: Highly compact deep autoencoder network architectures for on-device acoustic anomaly detection", Sensors, vol. 21, 2021. Get it here. McNally W., Vats K., Wong A., and McPhee J, "EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution with Weight Transfer", IEEE Access, vol. 9, pp. 139403-139414, 2021. Get it here. Lee J.R., Wang L., and Wong A, "EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-Time Facial Expression Recognition", Frontiers in Artificial Intelligence, vol. 3, 2021. Get it here. Wong A., Lu J., Dorfman A., McInnis P., Famouri M., Manary D., Lee J.R.H., and Lynch M, "Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT", Frontiers in Artificial Intelligence, vol. 4, 2021. Get it here. Liu L., Ouyang W., Wang X., Fieguth P., Chen J., Liu X., and Pietikainen M, "Deep Learning for Generic Object Detection: A Survey", International Journal of Computer Vision, vol. 127, pp. 261-318, 2020. Get it here. Gunraj H., Wang L., and Wong A, "COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images", Frontiers in Medicine, vol. 7, 2020. Get it here. Wang L., Lin Z.Q., and Wong A, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images", Scientific Reports, vol. 10, 2020. Get it here. Yu J., Tang S., Zhangzhong L., Zheng W., Wang L., Wong A., and Xu L, "A deep learning approach for multi-depth soil water content prediction in summer maize growth period", IEEE Access, vol. 8, pp. 199097-199110, 2020. Get it here. Wong A., Shafiee M.J., Chwyl B., and Li F, "Gensynth: A new way to understand deep learning", Electronics Letters, vol. 55, pp. 970-971, 2019. Get it here. Shafiee M.J., Chwyl B., Li F., Chen R., Karg M., Scharfenberger C., and Wong A, "StressedNets: Efficient feature representations via stress-induced evolutionary synthesis of deep neural networks", Neurocomputing, vol. 127, pp. 93-105, 2019. Get it here. Pfisterer K.J., Amelard R., Chung A.G., and Wong A, "A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prep", Journal of Food Engineering, vol. 127, pp. 220-235, 2018. Get it here. Shafiee M.J., Mishra A., and Wong A, "Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks", Neural Processing Letters, vol. 48, pp. 603-613, 2018. Get it here. Wong A., Shafiee M.J., and St Jules M, "MicronNet: A highly compact deep convolutional neural network architecture for real-time embedded traffic sign classification", IEEE Access, vol. 6, pp. 59803-59810, 2018. Get it here. Khodadad I., Shafiee J., Wong A., Kazemzadeh F., and Arlette J, "Deep tissue sequencing using hypodermoscopy and augmented intelligence to analyze atypical pigmented lesions", Journal of Cutaneous Medicine and Surgery, vol. 22, pp. 583-590, 2018. Get it here. Shafiee M.J., Wong A., and Fieguth P, "Deep Randomly-Connected Conditional Random Fields for Image Segmentation", IEEE Access, vol. 5, pp. 366-378, 2017. Get it here. Shafiee M.J., Chung A.G., Khalvati F., Haider M.A., and Wong A, "Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection", Journal of Medical Imaging, vol. 4, 2017. Get it here. Arlette J., Wong A., Khodadad I., and Kazemzadeh F, "Deep Tissue Sequencing Using Augmented Intelligence to Probe Melanocytic Lesions", Journal of Cutaneous Medicine and Surgery, vol. 21, pp. 572, 2017. Get it here. Wang L., Scott K.A., Xu L., and Clausi D.A, "Sea Ice Concentration Estimation during Melt from Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study", IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 4524-4533, 2016. Get it here. Shafiee M.J., Siva P., and Wong A, "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity", IEEE Access, vol. 4, pp. 1915-1924, 2016. Get it here. Shafiee M.J., Azimifar Z., Wong A., and Wang Y, "A deep-structured conditional random field model for object silhouette tracking", PLoS ONE, vol. 10, 2015. Get it here. Wong A., Shafiee M.J., Siva P., and Wang X.Y, "A deep-structured fully connected random field model for structured inference", IEEE Access, vol. 3, pp. 469-477, 2015. Get it here. Venema H.D., Calamai P.H., and Fieguth P, "Forest structure optimization using evolutionary programming and landscape ecology metrics", European Journal of Operational Research, vol. 127, pp. 423-439, 2005. Get it here. Chen X., Cantu F.J.P., Patel M., Xu L., Brubacher N.C., Scott K.A., and Clausi D.A, "A comparative study of data input selection for deep learning-based automated sea ice mapping", International Journal of Applied Earth Observation and Geoinformation, vol. 127, . Get it here. Li B., Palayew S., Li F., Abbasi S., Nair S., and Wong A, "PCBDet: An efficient deep neural network object detection architecture for automatic PCB component detection on the edge", Electronics Letters, vol. 60, . Get it here. Lu D., Schwartz S., Xu L., Shafiee M.J., Vinson N.G., Czarnecki K.J., and Wong A, "Integrating deep transformer and temporal convolutional networks for SMEs revenue and employment growth prediction", Expert Systems with Applications, vol. 127, . Get it here. Tai C.-E.A., Janes E., Czarnecki C., and Wong A, "Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images", Sensors, vol. 24, . Get it here.
Conference PapersBright J., Balaji B., Chen Y., Clausi D.A., and Zelek J.S, "PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2024. Get it here. Huang Y., Chen Y., and Zelek J, "Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2024. Get it here. Tai C.-E.A., Gunraj H., Hodzic N., Flanagan N., Sabri A., and Wong A, "Enhancing Clinical Support for Breast Cancer with Deep Learning Models Using Synthetic Correlated Diffusion Imaging", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024. Get it here. Patel M., Chen X., Xu L., Pena Cantu F.J., Noa Turnes J., Brubacher N.C., Clausi D.A., and Scott K.A, "The Influence of Input Image Scale on Deep Learning-Based Beluga Whale Detection from Aerial Remote Sensing Imagery", International Geoscience and Remote Sensing Symposium (IGARSS), 2023. Get it here. Chen X., Patel M., Cantu F.J.P., Park J., Turnes J.N., Jiang M., Xu L., Scott K.A., Clausi D.A., and Huang W, "The Influence of Input Variable Selection on Deep Learning-Based Sea Ice Parameter Inversion from Multi-Sensor Satellite Data", Oceans Conference Record (IEEE), 2023. Get it here. Boktor M., Ecclestone B., Pekar V., Dinakaran D., MacKey J.R., Fieguth P., and Reza P.H, "Deep-Learning-Based Virtual H&E Staining Using Total-Absorption Photoacoustic Remote Sensing (TA-PARS)", Optics InfoBase Conference Papers, 2022. Get it here. Raisi Z., Naiel M.A., Younes G., Fieguth P., and Zelek J, "Smart text reader system for people who are blind using machine and deep learning", Machine Learning Algorithms for Signal and Image Processing, 2022. Get it here. McNally W., Walters P., Vats K., Wong A., and McPhee J, "DeepDarts: Modeling keypoints as objects for automatic scorekeeping in darts using a single camera", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2021. Get it here. MacLean A., Abbasi S., Ebadi A., Zhao A., Pavlova M., Gunraj H., Xi P., Kohli S., and Wong A, "COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 P", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021. Get it here. Laschowski B., McNally W., Wong A., and McPhee J, "Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons", Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2021. Get it here. Abedi H., Ansariyan A., Morita P.P., Boger J., Wong A., and Shaker G, "Sequential Deep Learning for In-Home Activity Monitoring Using mm-Wave FMCW Radar", 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings, 2021. Get it here. Singh A., Mohammed A.R., Zelek J., and Lakshminarayanan V, "Interpretation of deep learning using attributions: Application to ophthalmic diagnosis", Proceedings of SPIE - The International Society for Optical Engineering, 2020. Get it here. Minhas M.S., and Zelek J, "Defect detection using deep learning from minimal annotations", VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020. Get it here. Hou Lee J.R., and Wong A, "TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-time Video Facial Expression Recognition", Proceedings - 2020 17th Conference on Computer and Robot Vision, CRV 2020, 2020. Get it here. Chung A.G., Fieguth P., and Wong A, "Assessing architectural similarity in populations of deep neural networks", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019. Get it here. Sengupta S., Singh A., Zelek J., and Lakshminarayanan V, "Cross-domain diabetic retinopathy detection using deep learning", Proceedings of SPIE - The International Society for Optical Engineering, 2019. Get it here. Shafiee M.S., Shafiee M.J., and Wong A, "Dynamic representations toward efficient inference on deep neural networks by decision gates", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019. Get it here. Wong A., Lin Z.Q., and Chwyl B, "AttoNets: Compact and efficient deep neural networks for the edge via human-machine collaborative design", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019. Get it here. Neher H., Arlette J., and Wong A, "Discovery radiomics for detection of severely atypical melanocytic lesions (saml) from skin imaging via deep residual group conv", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019. Get it here. Bidart R., and Wong A, "Triresnet: a deep triple-stream residual network for histopathology grading", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019. Get it here. Deglint J.L., Jin C., and Wong A, "Investigating the automatic classification of algae using the spectral and morphological characteristics via deep residual learn", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019. Get it here. Orumi M.A.B., Sepanj M.H., Famouri M., Azimifar Z., and Wong A, "Unsupervised deep shape from template", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019. Get it here. Kazemzadeh F., Li-Chang H., and Wong A, "Enhanced spectral lightfield fusion microscopy via deep computational optics for whole-slide pathology", Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019. Get it here. Kabiljagic D., and Wong A, "Resolution-enhanced digital epiluminescence microscopy using deep computational optics", Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019. Get it here. Ma A., Wong A., and Clausi D.A, "Deep learning-driven depth from defocus via active multispectral quasi-random projections with complex subpatterns", Proceedings - 2018 15th Conference on Computer and Robot Vision, CRV 2018, 2018. Get it here. Neher H., Vats K., Wong A., and Clausi D.A, "HyperStackNet: A hyper stacked hourglass deep convolutional neural network architecture for joint player and stick pose estimati", Proceedings - 2018 15th Conference on Computer and Robot Vision, CRV 2018, 2018. Get it here. Chung A., Fieguth P., and Wong A, "Nature vs. nurture: The role of environmental resources in evolutionary deep intelligence", Proceedings - 2018 15th Conference on Computer and Robot Vision, CRV 2018, 2018. Get it here. Lunscher N., and Zelek J, "Deep learning whole body point cloud scans from a single depth map", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018. Get it here. Wong A., Shafiee M.J., Li F., and Chwyl B, "Tiny SSD: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection", Proceedings - 2018 15th Conference on Computer and Robot Vision, CRV 2018, 2018. Get it here. Ma A., Wong A., and Clausi D, "Depth from defocus via active quasi-random point projections: A deep learning approach", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Chung A.G., Shafiee M.J., Fieguth P., and Wong A, "The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations Through Sexual Evolutionary Synthesis", Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2017. Get it here. Leopold H.A., Orchard J., Zelek J., and Lakshminarayanan V, "Use of gabor filters and deep networks in the segmentation of retinal vessel morphology", Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2017. Get it here. Zelek J., and Lunscher N, "Point Cloud Completion of Foot Shape from a Single Depth Map for Fit Matching Using Deep Learning View Synthesis", Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2017. Get it here. Lunscher N., and Zelek J, "Deep Learning Anthropomorphic 3D Point Clouds from a Single Depth Map Camera Viewpoint", Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2017. Get it here. Chwyl B., Chung A.G., Shafiee M.J., Fu Y., and Wong A, "DeepPredict: A deep predictive intelligence platform for patient monitoring", Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017. Get it here. Karimi A.-H., Chung A.G., Shafiee M.J., Khalvati F., Haider M.A., Ghodsi A., and Wong A, "Discovery radiomics via a mixture of deep ConvNet sequencers for multi-parametric MRI prostate cancer classification", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Kumar D., Wong A., and Taylor G.W, "Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017. Get it here. Shafiee M.J., Barshan E., Li F., Chwyl B., Karg M., Scharfenberger C., and Wong A, "Learning Efficient Deep Feature Representations via Transgenerational Genetic Transmission of Environmental Information During E", Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2017. Get it here. Dash P.P., Wong A., and Mishra A, "VeNICE: A very deep neural network approach to no-reference image assessment", Proceedings of the IEEE International Conference on Industrial Technology, 2017. Get it here. Clark T., Wong A., Haider M.A., and Khalvati F, "Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted MR images", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. Get it here. Shafiee M.J., Siva P., Fieguth P., and Wong A, "Efficient Deep Feature Learning and Extraction via StochasticNets", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2016. Get it here. Liu L., Fieguth P., Wang X., Pietikainen M., and Hu D, "Evaluation of LBP and deep texture descriptors with a new robustness benchmark", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. Get it here. Chung A.G., Shafiee M.J., and Wong A, "Image restoration via deep-structured stochastically fully-connected conditional random fields (DSFCRFs) for very low-light cond", Proceedings - 2016 13th Conference on Computer and Robot Vision, CRV 2016, 2016. Get it here. Kumar D., Wong A., and Clausi D.A, "Lung Nodule Classification Using Deep Features in CT Images", Proceedings -2015 12th Conference on Computer and Robot Vision, CRV 2015, 2015. Get it here. Xu C., Famouri M., Bathla G., Shafiee M.J., and Wong A, "High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-Wor", Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, . Get it here.