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fewshot learning, awesome meta learning, papers |
[TOC]
- awesome meta learning
- Distance metric learning for large margin nearest neighbor classification. JMLR 2009
- shows that non-parametric models (Weinberger & Saul, 2009) are able to capture local and heterogeneous structures in data.
- reducing intra-class variations of features has been highlighted in this paper (deeper backbone???)
- Andrej Karpathy note
- 我的筆記
- code (TF)
- code (PyTorch)
- code (TF) - 2
- code (Keras)
- 第一個提出 episodic training
- 第一個提出 mini-ImageNet
- we devised a new data set – miniImageNet – consisting of 60,000 colour images of size 84 × 84 with 100 classes, each having 600 examples.
- attention、memory network(multi-hopping)
- 和 Siamese Network 不同的是：Siamese Network 只學習一個 distance(或 similarity function)；而 Matching Network 直接 end-to-end 學習一個 nearest neighbor classifier
- 使用 cosine similarity 作為 metric
- code - official (PyTorch)
- code - official? (PyTorch)
- code (PyTorch)
- episodic training
- my paper note
- Since there is very few data available, a classifier should have a simple inductive bias 這句應該是出自這篇吧
- code - official (TF)
- 借鑑 ProtoNet 思想
- 提出 dataset: Fewshot-CIFAR100
- metric scaling
- 學習一個 scaling factor
$\alpha$ ，這樣可更好的輸出 metric 大小在合適的範圍
- 學習一個 scaling factor
- task conditioning
- 利用 prototype 的平均值構造 task representation，然後利用 task representation 來改變 feature extractor 的 function，即具有 adaptation 的能力
- auxiliary task co-training
- 也把所有 training data 用來訓練 feature extractor，做為輔助的 task 一起 train，能讓 feature 更 generalize
- 中文
- code - official (PyTorch fewshot)
- code - official (PyTorch zeroshot)
- code (PyTorch)
- episodic training
- 將 support set 和 query 的 embedding 做 concat，然後用 NN 計算相似程度。
- 同樣的 architecture 也可以用來做 ZSL，只要把 support set 換成 class semantic vector 即可
- metric-based [by CDFS&CloserLook]
- GNN as metric function
- ridge regression
- R2-D2
- metric-based [by CloserLook]
- 提出 dataset: CIFAR-FS
- metric-based [by DAPNA]
- metric learning via variational inference
- metric-based [by CDFS]
- follow "A Closer Look at Few-shot Classification" 的設定
- 根據 embedded query point 到每個 class subspace 的距離來 classify example
RepMet: Representative-based metric learning for classification and few-shot object detection. CVPR 2019
- metric-based [by 2020survey]
- Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster.
- semi-supervised and unsupervised setting
- learning to fine-tune?
- aim to learn a great initial parameter condition of base feature extractor, and then fine-tune the network to the unseen classes using just several examples within a few gradient steps. [by DAPNA]
- 上圖演算法 重要，李老師教的版本有點簡化了
- code - official? (TF)
- code - PyTorch
- 我的 NTU lecture 筆記
- 中文1
-
中文2
- 第一次 update 參數得到
$\theta_t'$ 時，使用 support set；而真正要更新$\theta$ 時，是使用 query set 得到的 loss
- 第一次 update 參數得到
- episodic training
- 任何使用 gradient descent 的模型都適用本方法
- 尋找一個模型的 initialize parameter
- shows that simply fine-tuning a convolutional neural network on a new classification task with very few samples has been shown to provide poor results
- 改良 MAML (with adaptive learning rate)
- 新增 inner loop learning rate
$\alpha$
- dataset: Sinusoid & lines
- initialization-based [by DAPNA]
- MAML++
- 就是一堆 trick
- iMAML
- 大幅改動了 MAML 的架構
- 二作 Chelsea Finn 大神
- code - official (TF)
- SOTA: LEO
- optimization-based [by CDFS]
- Hybrid: optimization-based + metric-based (RelationNet)
- 解決 MAML 不能很好的處理 high dim 的 data，即使 deeper network 也不好
- 用 (encoder+relation net) 對 data 做 latent code，然後 decode 出 w，再用 w 去算 loss 對 z 做 MAML，(最後得到的 w' 跟 x 做 內積完 softmax??
- OpenReview:
- contributions 有二：(1)本來 MAML 是固定 init params，現在他們把他變成低維 latent space。(2)依據 subproblem 的 input data 來決定 init params
- learning an optimizer
- equipped with an external or internal memory storage to store the transferable knowledge. [by DAPNA]
- recurrent-based [by CDFS]
- 最早用 external memory 解 FSL classification 的
- 別名：One-shot Learning with Memory-Augmented Neural Networks. arXiv'16
- 这篇论文解释了单样本学习与元学习的关系
- my paper note
- Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models.
- When new data is encountered, the conventional models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference.
- We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.
- LSTM-based meta learning [by authors]
- include the LSTM-based meta-learner for replacing the stochastic gradient decent optimizer. [by CloserLook]
- episodic training
- RNN-based approach [by DAPNA(Few-Shot Learning as Domain Adaptation)]
- code (PyTorch)
- code (PyTorch) - 2
- code (MXNet? Gluon)
- my paper note (unfinished)
- SOTA: SNAIL
- RNN-based approach [by DAPNA]
- SOTA: AdaResNet
- RNN-based [by DAPNA]
- 中文
- code - official (PyTorch)
- 只在一些
$(x^{(i)}, y^{(i)})$ 的 prediction loss 高於 threshold 時去更新 memory。因此相較於 differentiable memory(???)，計算成本降低了 - approximates probability distributions by remembering the most surprising observations
- algorithm can perform as well as state of the art baselines
- main contributions:
- surprise-based signal to write items to memory, not needing to learn what to write. So easier and faster to train, and minimizes how much data stored
- (不懂???) An integrated external and working memory architecture which can take advantage of the best of both worlds: scalability and sparse access provided by the working memory; and all-to-all attention and reasoning provided by a relational reasoning module.
- A training setup which steers the system towards learning an algorithm which approximates the posterior without backpropagating through the whole sequence of data in an episode.
- Conclusion
- We introduced a self-contained system which can learn to approximate a probability distribution with as little data and as quickly as it can. This is achieved by:
- putting together the training setup which encourages adaptation
- an external memory which allows the system to recall past events
- a writing system to adapt the memory to uncertain situations
- a working memory architecture which can efficiently compare items retrieved from memory to produce new predictions
- We showed that the model can
- Reach state of the art accuracy with a smaller memory footprint than other meta-learning models by efficiently choosing which data points to remember.
- Scale to very large problem sizes thanks to the use of an external memory module with sparse access.
- (不懂???) Perform fewer than 1-shot generalization thanks to relational reasoning across neighbors.
- We introduced a self-contained system which can learn to approximate a probability distribution with as little data and as quickly as it can. This is achieved by:
- learning to augment
- directly integrate the generator into a meta-learning algorithm for improving the classification accuracy. [by CloserLook]
- Data method: learned transformation
- Our approach is based on a modified auto-encoder, denoted delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier.
- proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class.
- the delta-encoder
- The simple key idea of this work is to change the meaning of
$E(X)$ from representing the "essence" of$X$ , to representing the delta, or "additional information" needed to reconstruct$X$ from$Y$ (an observed example from the same category).-
$E$ for encoder,$D$ for decoder
-
- The simple key idea of this work is to change the meaning of
- 暫時無 code (2020/4/15)
- generate weight approach (i think)
- SOTA
- reduce intra-class variance 的重要性
- generate weight approach (i think)
- code - official (TF)
- no further tuning steps are required compared to other meta-learning approaches
- code - official (PyTorch)
- SOTA: MetaOptNet
- few-shot 知乎
- motivation：将最近邻分类器换做SVM，提高分类器的判别能力。
- 方法：提取所有图像的特征，利用SVM得到所有分类器的参数w （对偶的SVM）对测试图像进行分类，优化特征提取器参数
- 暫時無 code (2020/4/15)
- Learn a attention(mask) to pay more attention on the part of the images
Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data. ECCV 2018
- done by sharing the first several layers of two networks to learn the generic information, while learning a different last layer to deal with different output for each task.
- 有做 cross-domain，但 few-shot???
- code - official (PyTorch)
- 好像是跟 CloserLook 一樣的設定???
- 實驗(dataset) 跟 CloserLook 不一樣
- natural image -> cartoon-like image
- code - official (PyTorch)
- train on ImageNet (miniImagenet? (perspective/natural images/color)
- performance 和 「dataset 與 imagenet 相似度」有關
- test on
- CropDisease (perspective(遠景?)/natural images/color)
- EuroSAT (no perspective/natural images/color)
- ISIC (no perspective/medical images/color)
- ChestX (no perspective/medical images/grayscale)
- cross domain 時，meta-learning 方法比 fine-tune 糟
- 可以把這篇當成一個 survey 吧
- 找 hyperparameter，怎感覺 approach 怪怪
- 而且也做 ensemble??
- train/val/test:
- code - official (PyTorch) from CloserLookFewShot
- 有做 cross-domain experiments (miniImagent -> CUB)
- dataset: CIFAR-FS, CUB, miniImagenet, tieredImagenet
- 就 Manifold Mixup + Semi-supervised (+meta-learning?)
- 感覺沒什麼 novelty，而且還用 testing set 調參數?
- 感覺可以套自己的方法上去
- 是從 CloserLook 改的
- cross-domain performance 還不錯
- 有做 cross-domain experiments
- cross domain train/val/test:
- code - official (PyTorch)【連結失效】
- 中文
- 可以把 few-shot scenario 的 label shift 看成是一種 domain shift
- 建構兩個 sub-episode (沒有 class overlap) 來模擬 label(domain) shift
- 雖然主軸 few-shot 但也有做 cross-domain 實驗
- cross-domain 似乎完全 follow CloserLook 設定
- 可比較
- SOTA: DAPNA
Few-Shot Classification on Unseen Domains by Learning Disparate Modulators. arXiv'1909, ICLR 2020 rejected
- 怎麼感覺跟我的 idea 很像
- 別名：Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators
- multi-domain dataset: Visual Decathlon
- procedure
- 拿 source 來 train base network
- 每個 domain 都 train 一個 per-layer module
$\alpha_i$ - 訓練一個 model selction network 來 predict 最好的 network (DoS)
- 或者直接 Averaging all network (DoA)
- 我覺得他的 DoS (在unseen domain) 的 performance 好廢，強的只有 DoA，可是 DoA 就沒 novelty，難怪沒上
- 不用 meta-learning，而是用 ensemble DNN 的方式達到 SOTA 效果
- introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity
- 有評估 cross domain 的 performance
- even a single network obtained by distillation yields state-of-the-art results.
- cooperation v.s. diversity
- cooperation: encourages non-gound truth prob(紅色) to be similar
- diversity: encourages orthogonality
- seems doing causal inference to fast adapt under domain shift
- code - official (PyTorch)
- metric-based few-shot classification often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains.
- core idea is to use feature-wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage.
- further apply a learning-to-learn approach to search for the hyper-parameters of the feature-wise transformation layers.
- optimize the feature-wise transformation layers so that the model can work well on the unseen domains after training the model using the seen domains.
- modulated activations
$\hat z_{c,h,w}=\gamma_c\times z_{c,h,w}+\beta_c$ $\gamma\sim N(1,\text{softplus}(\theta_\gamma))$ $\beta\sim N(0,\text{softplus}(\theta_\beta))$ -
$\theta_\gamma,\theta_\beta$ are hyperparameters.
- single source domain experiments
- train on mini-Imagenet (pseudo-seen domain???)
- multi- source domain experiments
- miniImagenet/CUB/Cars/Places/Plantae (pseudo-unseen domain)
- cross domain experiment, but seems not explicitly deal with domain shift in proposed method
- trained on mini-Imagenet
- tested on
- mini-Imagenet
- Caltech-101
- CUB-200
- Stanford Dogs & Cars
- Revisit triplet network, propose K-tuplet siamese network
-
$K$ negative samples in a batch - semi-hard mining (wats difference with facenet???)
-
Label Efficient Learning of Transferable Representations across Domains and Tasks. NIPS 2017 (Li Fei-Fei)
- Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain.
- Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach.
- initialize the CNN for the target tasks in the target domain by a pre-trained CNN learning from source tasks in source domain. During training, they use an adversarial loss calculated from representations in multiple layers of CNN to force the two CNNs projects samples to a task-invariant space.
- ProtoNet + CycleGAN?
reviewer comment at OpenReview
- The proposed approach consists of combining a known few shot learning model, prototypical nets, together with image to image translation via CycleGAN for domain adaptation. Thus the algorithmic novelty is minor and amounts to combining two techniques to address a different problem statement.
- though meta learning could be a solution to learn with few examples, the solution being used in this work is not meta learning and so should not be in the title to avoid confusion.
- code - official (PyTorch)
- 中文
- transformer + ProtoNets?
- SOTA: EA-FSL / FEAT
- 實驗也有做 cross-domain，但是也一樣，source 和 target 的 label space 相同
- 似乎沒用到 target task 的 data?
- my paper note
- title 的字並沒有打錯ㄛ
- 需要 target domain unlabeled data
- My paper note
- code - official (TF)
- given only one example of each new class. Can we transfer knowledge learned by oneshot learning from one domain to another?
- propose a domain adaption framework based on adversarial networks.
- This framework is generalized for situations where the source and target domain have different labels.
- use a policy network, inspired by human learning behaviors, to effectively select samples from the source domain in the training process. This sampling strategy can further improve the domain adaption performance.
- 中文
- code - official (PyTorch)
- 提出兩個普通 baseline，發現許多情況可以和 SOTA 的 fewshot learning 媲美
- 比較的 SOTA 方法：MatchingNet、ProtoNet、RelationNet、MAML
- domain 差異小的情況下(例如CUBS)，隨著 baseNN 越強，不同 SOTA 方法的差異越小
- domain 差異大的情況下(例如miniImageNet)，隨著 baseNN 越強，不同 SOTA 方法的差異越大
- 有領域飄移情況發生時，SOTA 方法甚至沒有 baseline 表現好
- 特別強調 SOTA 在 domain adaptation 做得不好
- Reviewers' Comment
- The conclusion from the network depth experiments is that “gaps among different methods diminish as the backbone gets deeper”. However, in a 5-shot mini-ImageNet case, this is not what the plot shows. Quite the opposite: the gap increased. Did I misunderstand something? Could you please comment on that?
- 跟我想問的問題一樣
- Authors' Answer: Sorry for the confusion. As addressed in 4.3, gaps among different methods diminish as the backbone gets deeper in the CUB dataset. In the mini-ImageNet dataset, the results are more complicated due to the domain difference. We further discuss this phenomenon in Section 4.4 and 4.5. We have clarified related texts in the revised paper.
- The conclusion from the network depth experiments is that “gaps among different methods diminish as the backbone gets deeper”. However, in a 5-shot mini-ImageNet case, this is not what the plot shows. Quite the opposite: the gap increased. Did I misunderstand something? Could you please comment on that?
- Microsoft AI & Research, IBM Research AI, JD AI Research
- Sentence Classification Services / Omniglot / Amazon Reviews
- Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels.
- we proposed a meta metric learner for few-shot learning, which is a combination of an LSTM meta-learner and a base metric classifier.
- The proposed method takes several advantages such as is able to handle unbalanced classes as well as to generate task-specific metrics.
- We test our approach in the ‘k-shot N-way’ few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers.
- contributions
- improve the existing few-shot learning work to handle various class labels (not only k-shot N-way)
- enable the model to learn task specific metrics via training a meta learner
- we are the first to investigate few-shot deep learning methods in the text domains.
- 別名 "Revisiting Meta-Learning as Supervised Learning"
- 以 supervised learning 的方式去理解 meta-learning
- (No Deep Learning, but worth reading)
- 複雜的 meta-learning 結構其實沒這麼屌
All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning. arXiv'1911
- Data method: transform other dataset???
- semi-supervised setting to support label propagation
- code - official (PyTorch, Intel MKL, faiss)
- code - official (TF)
- 和 ProtoNet 同作者
- SOTA: Soft k-Means
- 提出 dataset: tiered-imagenet
- 中文(清楚)
- semi-supervised few-shot task 分兩種情況
- unlabeled support 的 class 和 labeled support 相同
- unlabeled support 的 class 和 labeled support 不同
- 这种具有误导信息的样本我们称为干扰项（distractor）
- semi-supervised few-shot task 分兩種情況
- 概念跟樓下那篇似乎有點像
- SOTA: TPN
- 中文
- code - official (TF)
- code - official (PyTorch)
- 有點像是結合 meta-learning 跟 semi-supervised learning
- 將 support set 的 label 在 query set 中傳遞
- My Paper Note(unfinished)
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph. IJCAI 2019
- 應該是利用 高level 低 level 的 class 來把 prototype 做 propagation
- 也用到 attribute
- CUB
- code - official (PyTorch)
- Chinese1
- Chinese2
- In this work, we take feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders.
- 這篇跟我本來想到的 idea 一樣，居然有人做過了，機車
- 不過也許還沒做 domain shift?
- 對，他沒做 domain shift 哈哈哈哈
- Reviewer Comment
- this paper is not really doing few-shot learning, because according to section 3.2. and the experiments, the authors use the test labels in order to know which word embeddings to assign to each sample: "[...] containing label embeddings of all categories in D_train ∪ D_test". In other words, the authors use the labels (which are the goal of the classification task) to find the match between the two input modalities (to know what Glove vector to assign to each image).
- the experiments compare the results only between this multimodal approach and visual approaches. I believe using the Glove embeddings alone (no visual input) could give very good results on their own, and it is thus crucial for the authors to compare with this scenario too.
- the explanation for why you chose this form for lambda_c is unclear: "A very structured semantic space is a good choice for conditioning."
- few-shot 知乎
- motivation：在特征上做数据增广不足以考察类内的变化-->在语义空间上做数据增广。
- 方法：
- 提取类别的语义空间（人工标注的语义属性空间；word2vec得到的语义word空间）
- 联合训练一个特征提取器和视觉特征空间到语义空间的映射。
- 对于测试的训练图像，将其映射到语义空间，在语义空间做数据增广（加入高斯噪声或者映射到相应的相似的类别），再映射到语义空间，得到增广后的特征。
- 将增广的特征和原有的特征联合训练一个分类器。
- utilize semantic information [by DAPNA]
- metric-based [by 2020survey]
- problem
- This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes
- motivation：对于一个novel类只给了一个样本，但是给定的图像可能含有其他无关的信息，因此利用一个注意力机制，只关注样本中于目标类别相关的区域。
- 方法：
- 利用word2vec提取类别的语义信息a，CNN提取图像的视觉信息x。
- 类似self-attention机制，以a作为query，x作为gallery和value，生成多个attention map
- 根据生成的attention map对图像特征加权求和，得到图像的最后特征
- 在训练集上对图像特征分类，训练整个网络
- 测试时，输入一张训练图像和类别得到相应的特征，对测试图像，分别输入多个类别的语义信息，得到多个图像特征，最近邻比对
- any-way, any-shot
- code - official (TF+Keras)
- support set 跟 query set 都跟 training set 的 data 算 KL-divergence，然後看 query set 跟 support set 的哪個 data 最像
- 好像不是做 domain shift 的 @@
- 分別對 few-shot learning, generalized zero-shot learning, domain adaptation 做了實驗
- Abstract
- We propose a novel visual attribute encoding method that encodes each image as a low-dimensional probability vector composed of prototypical part-type probabilities.
- At test-time we freeze the encoder and only learn/adapt the classifier component to limited annotated labels in FSL; new semantic attributes in ZSL.
- motivation：超参数设置十分重要，利用meta-learning对每一层学习一个超参数；一个learner通常不稳定，在MAML的机制上学习如何融合多个learner。
- code - reproduced (TF)
- code - reproduced (PyTorch): under-developed
- 中文 (僅佔小部份篇幅)
- 作者blog
- 方法 (改良 MAML)
- 用 training set 所有 sample 訓練一個 NN 來 classify，得到一個 feature extractor
- sample 多個 task，用 MAML 根據 support set 更新 classifier(最後一層?) 參數，再利用 query set 計算 gradient，微調 initial parameter
- Hard Task(HT) meta-batch
- 故意選一些 fail 的 task 並重組那些 data 成為 harder tasks 以用來 adverse re-training，希望 meta-learner 能在困難中成長 0.0
- code - official (PyTorch)
- few-shot 知乎
- motivation: 全局特征对于小样本数据不是很友好，考虑局部特征，会过滤掉干扰物体以及背景的信息。
- 做法：对于query图像的每个局部特征都计算一个与support set图像的相似性
- 提出了新的 scenario: Blending-target Domain Adaptation (BTDA)
- 提出了 Adversarial Meta-Adaptation Network (AMEAN)
- 作者導讀 中文
- 直接最大化初始模型在不同类别上的熵（Entropy Maximization）来实现对任务的无偏性
- optimized-based meta-learning
- 引用 closerlook
- 根據 support set 得到一個 channel attention，對所有的 image 做 channel attention
- few-shot 知乎
- motivation：对于few-shot的support set，现有的方法都是单独为其提取特征，没有考虑这个task的更具有判别性的特征。利用support set的所有图像的信息，提取具有判别性的特征。
- 方法：对support set生成一个channel attention。
- motivation: 分类的交叉熵loss只会关注最显著的区域，会造成提取特征的严重过拟合。通过约束模型更加关注其他区域的特征，提高特征提取器的泛化能力。
- 方法：
- 输入图像，经过特征提取器得到特征F，经过分类器，得到概率分布，以及entropy loss
$l$ - 求的entropy loss对输入特征F的梯度，在初始的M上加上其梯度（新得到M使entropy loss更大）。
- 将新得到的M与输入特征F相乘，求平均，经过分类器，得到cross entropy loss
$l_1$ - 将F再经过多个卷积层，使其空间维度为1，经过分类器，得到cross entropy loss
$l_2$ - 通过
$l_1+l_2$ 优化网络
- 输入图像，经过特征提取器得到特征F，经过分类器，得到概率分布，以及entropy loss
- code - official (Chainer)
- 不是 domain shift
- episodic training
- learn a network & a set of per-class reference vectors
- linearly projected features as task-specific conditioning
- evaluation dataset: omniglot, miniImagenet, tieredImagenet
- test under various few-shot scenarios
- We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable.
- CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation.
- renamed to: Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification ?
- code - official (PyTorch)
- code - official (PyTorch)
- aims to deal with noisy data
- code - official (PyTorch)
- 用 meta-learning 來學 Visual Navigation task
- few-shot + GAN
- They learn separate embedding for source and target tasks in different domains to map them into a task-invariant space, then learn a shared classifier to classify samples from all tasks.
- We propose a novel memory architecture, the Neural Bloom Filter, which is able to achieve significant compression gains over classical Bloom Filters and existing memory-augmented neural networks
- focus on gradient-based meta-learning
- MAML 作者
- propose a few-shot learning model that is tailored specifically for regression tasks
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning. arXiv'1903
- Han-Jia Ye
- metric learning
- meta learning
- Chelsea Finn, UC Berkeley
- MAML
- Pieter Abbeel, UC Berkeley
- Erin Grant, UC Berkeley
- Raia Hadsell, DeepMind
- Misha Denil, DeepMind
- Adam Santoro, DeepMind
- Sachin Ravi, Princeton University
- David Abel, Brown University
- Brenden Lake, _Facebook AI Research_fjdslfjsl
- TorchMeta (Library), [GitHub]
- omniglot, mini-Imagenet
- tiered-Imagenet
- CIFAR-FS
- Fewshot-CIFAR100
- Caltech-UCSD Birds (CUBS 200)
- Double MNIST (Multi-Digit MNIST)
- Triple MNIST (Multi-Digit MNIST)
- Toy Datasets (Few-Shot Regression)
- Sine waves
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. 2017
- Harmonic functions
- Uncertainty in Multitask Transfer Learning. 2018
- Sinusoid & lines
- Probabilistic Model-Agnostic Meta-Learning. 2018
- Sine waves
- Meta-Dataset
- Few-Shot Object Detection Dataset
- VPE
- traffic sign datasets
- GTSRB
- TT100K
- logo datasets
- BelgaLogos
- FlickrLogos-32
- TopLogos-10
- traffic sign datasets