Invariant Information Clustering for Unsupervised Image Classification and Se...harmonylab
紹介論文
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Xu J, João F. Henriques, Andrea Vedaldi
出典:Xu J, João F. Henriques, Andrea Vedaldi:Invariant Information Clustering forUnsupervised Image Classification and Segmentation, International Conference on Computer Vision (ICCV 2019), Seoul, Korea
概要:本論文では、正解ラベルを必要としない教師なし学習手法IICを提案しています。元画像に一般的なランダム変換を加えたペアを作成し、元画像とペアの相互情報量を最大化するよう学習を行います。画像のクラス分類・セグメンテーションタスクにおいて、8つのベンチマークでSOTAを達成しています。さらに、半教師あり学習にすることで、従来の教師あり学習精度を超える結果を得ています
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
[Paper Reading] Causal Bandits: Learning Good Interventions via Causal InferenceDaiki Tanaka
paper reading : [NIPS 2016] Causal Bandits: Learning Good Interventions via Causal Inference
https://papers.nips.cc/paper/6195-causal-bandits-learning-good-interventions-via-causal-inference.pdf
Invariant Information Clustering for Unsupervised Image Classification and Se...harmonylab
紹介論文
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Xu J, João F. Henriques, Andrea Vedaldi
出典:Xu J, João F. Henriques, Andrea Vedaldi:Invariant Information Clustering forUnsupervised Image Classification and Segmentation, International Conference on Computer Vision (ICCV 2019), Seoul, Korea
概要:本論文では、正解ラベルを必要としない教師なし学習手法IICを提案しています。元画像に一般的なランダム変換を加えたペアを作成し、元画像とペアの相互情報量を最大化するよう学習を行います。画像のクラス分類・セグメンテーションタスクにおいて、8つのベンチマークでSOTAを達成しています。さらに、半教師あり学習にすることで、従来の教師あり学習精度を超える結果を得ています
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
[Paper Reading] Causal Bandits: Learning Good Interventions via Causal InferenceDaiki Tanaka
paper reading : [NIPS 2016] Causal Bandits: Learning Good Interventions via Causal Inference
https://papers.nips.cc/paper/6195-causal-bandits-learning-good-interventions-via-causal-inference.pdf
[Paper reading] L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR S...Daiki Tanaka
This document proposes two new algorithms, L-SHAPLEY and C-SHAPLEY, for interpreting black-box machine learning models in an instance-wise and model-agnostic manner. L-SHAPLEY and C-SHAPLEY are approximations of the SHAPLEY value that take graph structure between features into account to improve computational efficiency. The algorithms were evaluated on text and image classification tasks and were shown to outperform baselines like KERNELSHAP and LIME, providing more accurate feature importance scores according to both automatic metrics and human evaluation.
Selective inference is a statistical framework that accounts for selection bias when using feature selection methods like Lasso. When features are selected from a larger set for inclusion in a model, directly interpreting p-values from fitting that model can be misleading without correcting for the selection process. Selective inference provides adjusted confidence intervals to correctly assess whether selected features have statistically significant effects while controlling for the selection bias introduced by the feature selection method.
Anomaly Detection with VAEGAN and Attention [JSAI2019 report]Daiki Tanaka
Daiki Tanaka from Kyoto University proposes a method to detect anomaly images using deep generative models while correcting for noisy areas that could be misidentified as anomalies. The method trains an autoencoder with a GAN discriminator that learns to focus on major image areas rather than noise. At test time, it calculates the reconstruction error between the original and reconstructed image, weighted by the discriminator's attention weights to discount noisy pixels. On MNIST data with added noise, the method outperforms other deep generative models in anomaly detection as measured by ROC-AUC scores.
[Paper Reading] Attention is All You NeedDaiki Tanaka
The document summarizes the "Attention Is All You Need" paper, which introduced the Transformer model for natural language processing. The Transformer uses attention mechanisms rather than recurrent or convolutional layers, allowing for more parallelization. It achieved state-of-the-art results in machine translation tasks using techniques like multi-head attention, positional encoding, and beam search decoding. The paper demonstrated the Transformer's ability to draw global dependencies between input and output with constant computational complexity.
Local Outlier Detection with InterpretationDaiki Tanaka
This paper proposes a method called Local Outlier Detection with Interpretation (LODI) that detects outliers and explains their anomalousness simultaneously. LODI first selects a neighboring set for each outlier candidate using entropy measures. It then computes an anomaly degree for each object based on its deviation from neighbors in a learned 1D subspace. Finally, LODI interprets outliers by identifying a small set of influential features. Experiments on synthetic and real-world data show LODI outperforms other methods in outlier detection and provides intuitive feature-based explanations. However, LODI's computation is expensive and it assumes linear separability, which are limitations for future work.
1) The document discusses LIME (Local Interpretable Model-Agnostic Explanations), a method for explaining the predictions of any machine learning model. LIME works by training an interpretable model locally around predictions to approximate the original model.
2) Experiments show that LIME explanations help human subjects select better performing classifiers, identify features to improve classifiers, and gain insights into how classifiers work.
3) SP-LIME is introduced to select a representative set of predictions to provide a global view of a model, by maximizing coverage of important features.
The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain ...Daiki Tanaka
This document summarizes a research paper presented at Kyoto University. The paper proposes a framework called CBEP to prioritize broadcast emails. CBEP addresses three challenges: sampling user feedback, selecting optimal source domains to transfer knowledge from, and predicting email priority. It uses a matrix factorization technique called alternating least squares to model user and item latent factors from feedback data. The method was tested on a dataset of emails and view logs from Samsung mailing lists.
The Limits of Popularity-Based Recommendations, and the Role of Social TiesDaiki Tanaka
This document summarizes a research paper that models how recommender systems can influence product popularity in markets. It presents a model that simulates user purchases based on personal preferences and recommendations from social connections. Experiments on this model using real social network data found that the recommender system did not significantly distort the market shares of different products. However, adding a "super-node" that strongly recommends one product to all users did substantially distort the market in favor of that product.
Learning Deep Representation from Big and Heterogeneous Data for Traffic Acci...Daiki Tanaka
The document describes a study that used deep learning to predict traffic accident risk levels based on human mobility data. The researchers trained a stacked denoising autoencoder model on GPS records from 1.6 million people to learn representations of human mobility patterns. They then used these representations along with 300,000 records of past traffic accidents to predict accident risk levels on a grid map. The model outperformed baseline methods like decision trees and logistic regression in predicting traffic accident risk levels.
3. ● private test datasetに擬似ラベルを付与し、追加の訓練データとして利用することで private
test datasetに対する汎化性能を向上させる試みがある (pseudo-labelingと呼ばれる)
a. Devデータでモデル1を作る
b. モデル1を使ってTestデータに対して推論を行い、確信度の高いデータに対して擬似ラベル を
割り振る
c. Devデータと上記で作った擬似ラベル付き Testデータを混ぜて新しいモデル 2を作る
d. モデル2を使ってTestデータの推論を行う
● 真のラベルを利用してないのに何で汎化性能が上がるのか?
self-trainingの例:
kaggleでしばしば見かける、semi-supervised learning的な方法