Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network

• URL: http://arxiv.org/abs/2102.05176v2
• Date: Thu, 11 Feb 2021 16:04:28 GMT
• Title: Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
• Authors: Tom\'a\v{s} Chobola, Daniel Va\v{s}ata, Pavel Kord\'ik
• Abstract summary: This paper describes second best submission in the competition. Our meta learning approach modifies the distribution of classes in a latent space produced by a backbone network for each class. For this task, we utilize optimal transport mapping using the Sinkhorn algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MetaDL Challenge 2020 focused on image classification tasks in few-shot settings. This paper describes second best submission in the competition. Our meta learning approach modifies the distribution of classes in a latent space produced by a backbone network for each class in order to better follow the Gaussian distribution. After this operation which we call Latent Space Transform algorithm, centers of classes are further aligned in an iterative fashion of the Expectation Maximisation algorithm to utilize information in unlabeled data that are often provided on top of few labelled instances. For this task, we utilize optimal transport mapping using the Sinkhorn algorithm. Our experiments show that this approach outperforms previous works as well as other variants of the algorithm, using K-Nearest Neighbour algorithm, Gaussian Mixture Models, etc.

Related papers

• Distributive Pre-Training of Generative Modeling Using Matrix-Product States [0.0]
  We consider an alternative training scheme utilizing basic tensor network operations, e.g., summation and compression. The training algorithm is based on compressing the superposition state constructed from all the training data in product state representation. We benchmark the algorithm on the MNIST dataset and show reasonable results for generating new images and classification tasks.
  arXiv  Detail & Related papers  (2023-06-26T15:46:08Z)
• Resource saving taxonomy classification with k-mer distributions and machine learning [2.0196229393131726]
  We propose to use $k$-mer distributions obtained from DNA as features to classify its taxonomic origin. We show that our approach improves the classification on the genus level and achieves comparable results for the superkingdom and phylum level.
  arXiv  Detail & Related papers  (2023-03-10T08:01:08Z)
• Classified as unknown: A novel Bayesian neural network [0.0]
  We develop a new efficient Bayesian learning algorithm for fully connected neural networks. We generalize the algorithm for a single perceptron for binary classification in citeH to multi-layer perceptrons for multi-class classification.
  arXiv  Detail & Related papers  (2023-01-31T04:27:09Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Langevin Monte Carlo for Contextual Bandits [72.00524614312002]
  Langevin Monte Carlo Thompson Sampling (LMC-TS) is proposed to directly sample from the posterior distribution in contextual bandits. We prove that the proposed algorithm achieves the same sublinear regret bound as the best Thompson sampling algorithms for a special case of contextual bandits.
  arXiv  Detail & Related papers  (2022-06-22T17:58:23Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• Gated recurrent units and temporal convolutional network for multilabel classification [122.84638446560663]
  This work proposes a new ensemble method for managing multilabel classification. The core of the proposed approach combines a set of gated recurrent units and temporal convolutional neural networks trained with variants of the Adam gradients optimization approach.
  arXiv  Detail & Related papers  (2021-10-09T00:00:16Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)
• Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks [132.29772160843825]
  Conditional Generative Adversarial Networks (cGAN) were designed to generate images based on the provided conditions. Existing methods have used the same generating architecture for all classes. This paper presents a novel idea that adopts NAS to find a distinct architecture for each class.
  arXiv  Detail & Related papers  (2020-06-25T07:05:28Z)
• Generalized Zero and Few-Shot Transfer for Facial Forgery Detection [3.8073142980733]
  We propose a new transfer learning approach to address the problem of zero and few-shot transfer in the context of forgery detection. We find this learning strategy to be surprisingly effective at domain transfer compared to a traditional classification or even state-of-the-art domain adaptation/few-shot learning methods.
  arXiv  Detail & Related papers  (2020-06-21T18:10:52Z)
• A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application [0.0]
  The aim of this work is to propose a meta-algorithm for automatic classification in the presence of discrete binary classes. Overlapping classes are described by the presence of ambiguous areas in feature space with a high density of points belonging to both classes. The algorithm proposed is a variant of the classical boosted decision tree which is known to be one of the most successful analysis techniques in experimental physics.
  arXiv  Detail & Related papers  (2020-01-19T18:22:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.