Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Memory-Efficient Incremental Learning Through Feature Adaptation

We introduce an approach for incremental learning that preserves feature descriptors of training images from previously learned classes, instead of the images themselves, unlike most existing work. Keeping the much lower-dimensional feature embeddings of images reduces the memory footprint significantly. We assume that the model is updated incrementally for new classes as new data becomes available sequentially.This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding original training images. Feature adaptation is learned with a multi-layer perceptron, which is trained on feature pairs corresponding to the outputs of the original and updated network on a training image. We validate experimentally that such a transformation generalizes well to the features of the previous set of classes, and maps features to a discriminative subspace in the feature space. As a result, the classifier is optimized jointly over new and old classes without requiring old class images. Experimental results show that our method achieves state-of-the-art classification accuracy in incremental learning benchmarks, while having at least an order of magnitude lower memory footprint compared to image-preserving strategies

Karakteristik Air Tanah Dangkal Kota Semarang Pada Musim Penghujan Berdasarkan Pendekatan Isotop Stabil (18O, 2H) dan Kimia Air

Pada bulan Maret 2014 telah dilakukan penelitian air tanah di wilayah Kota Semarang dengan tujuan untuk mengetahui karakteristik air tanah dangkal pada saat musim penghujan melalui pendekatan isotop stabil (18O, 2H) dan kimia air yang didukung dengan data hidrogeologi setempat. Sejumlah sampel air tanah dangkal diambil di beberapa lokasi dengan kedalaman antara 0 — 35 m di bawah permukaan tanah setempat (dbpts). Analisis isotop stabil 18O dan 2H serta kimia air dilakukan di laboratorium Hidrologi, Pusat Aplikasi Isotop dan Radiasi, BATAN Jakarta. Hasil analisis isotop stabil 18O dan 2H menunjukkan bahwa sekitar 63 % air tanah cenderung berada di dekat garis meteorik Semarang dan sekitar 37 % sisanya mengalami evaporasi, interaksi dengan oksida batuan dan sedikit pengaruh interaksi atau mixing dengan air laut. Dari hasil analisis kimia air dengan ionic balancesekitar 3 % menunjukkan bahwa pada saat musim penghujan akuifer air tanah dangkal di wilayah Kota Semarang didominasi oleh ion bikarbonat (HCO3-) dengan tipe air didominasi CaHCO3. Sedangkan dari data parameter Chloride Bicarbonate Ratio, sekitar 24 % air tanah dangkal di wilayah Kota Semarang terindikasi mengalami intrusi air laut dansisanya sekitar 76 % masih menunjukkan karakteristik sebagai air tanah tawar.Kata kunci : karakteristik air tanah, air tanah dangkal, Semarang, musim penghujan, isotopstabil dan kimia ai

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Investigating the Benefits of Exploiting Incremental Learners Under Active Learning Scheme

Part 2: AI Anomaly Detection - Active LearningInternational audienceThis paper examines the efficacy of incrementally updateable learners under the Active Learning concept, a well-known iterative semi-supervised scheme where the initially collected instances, usually a few, are augmented by the combined actions of both the chosen base learner and the human factor. Instead of exploiting conventional batch-mode learners and refining them at the end of each iteration, we introduce the use of incremental ones, so as to apply favorable query strategies and detect the most informative instances before they are provided to the human factor for annotating them. Our assumption about the benefits of this kind of combination into a suitable framework is verified by the achieved classification accuracy against the baseline strategy of Random Sampling and the corresponding learning behavior of the batch-mode approaches over numerous benchmark datasets, under the pool-based scenario. The measured time reveals also a faster response of the proposed framework, since each constructed classification model into the core of Active Learning concept is built partially, updating the existing information without ignoring the already processed data. Finally, all the conducted comparisons are presented along with the appropriate statistical testing processes, so as to verify our claim