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1. Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions

   Jin, Tian; Liu, Zhun; Yan, Shengjia; Eichenberger, Alexandre; Morency, Louis-Philippe (January 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   Transfer learning using ImageNet pre-trained models has been the de facto approach in a wide range of computer vision tasks. However, fine-tuning still requires task-specific training data. In this paper, we propose N3 (Neural Networks from Natural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model. N3 leverages language descriptions to generate parameter adaptations as well as a new task-specific classification layer for a pre-trained neural network, effectively “fine-tuning” the network for a new task using only language descriptions as input. To the best of our knowledge, N3 is the first method to synthesize entire neural networks from natural language. Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero-shot image classification benchmarks. We also demonstrate a simple method to help identify keywords in language descriptions leveraged by N3 when synthesizing model parameters. 

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2. Efficient K-Shot Learning with Regularized Deep Networks

   YOO, DONGHYUN; FAN, HAOQI; BODDETI, VISHNU NARESH; KITANI, KRIS M. (October 2017, AAAI)

   Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data. Despite the effectiveness of fine-tuning, itis often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and over-fitting, is due in part to the large number of parameters used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and inter-cluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that shares similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data. To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer. Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than10% 

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3. Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

   https://doi.org/10.3389/frai.2022.780405  

   Ardalan, Zaniar; Subbian, Vignesh (February 2022, Frontiers in Artificial Intelligence)

   Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time. 

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4. Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

   https://doi.org/10.1109/MIPR49039.2020.00020  

   Tian, Haiman; Chen, Shu-Ching; Shyu, Mei-Ling (August 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR))

   null (Ed.)

   Abstract: Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks. 

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5. Transfer Learning Pre-training Dataset Effect Analysis for Breast Cancer Imaging

   https://doi.org/10.29007/nns7  

   Bulathsinghalage, Chanaka; Liu, Lu (March 2022, EPiC Series in Computing)

   Comparing with natural imaging datasets used in transfer learning, the effects of med- ical pre-training datasets are underexplored. In this study, we carry out transfer learning pre-training dataset effect analysis in breast cancer imaging by evaluating three popular deep neural networks and one patch-based convolutional neural network on three target datasets under different fine-tuning configurations. Through a series of comparisons, we conclude that the pre-training dataset, DDSM, is effective on two other mammogram datasets. However, it is ineffective on an ultrasound dataset. What is more, fine-tuning may mask the inefficacy of a pre-training dataset. In addition, the efficacy/inefficacy of DDSM on the target datasets is corroborated by a representational analysis. At last, we show that hybrid transfer learning cannot mitigate the masking effect of fine-tuning. 

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