Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences , such that new tasks be learned from small amounts of data . However , a critical challenge in few-shot learning is task ambiguity : even when a powerful prior can be meta-learned from a large number of prior tasks , a small dataset for a new task can simply be too ambiguous to acquire a single model ( e.g. , a classifier ) for that task that is accurate . In this paper , we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution . Few-shot learning in computer vision is a very challenging task . Many approaches have been proposed to tackle the few-shot learning problem . Meta learning , a method of learning to learn , is introduced into few-shot learning problem and has achieved pretty good results . Meta-learning has been proposed as a framework to address the challenging few-shot learning setting . The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available . The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled . In this few-shot learning scenario , alignment and separation of semantic probability distributions is difficult because of the lack of data . We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes , it is possible to effectively address the supervised adaptation problem . This is achieved by using a decaying parameter chosen according to the slope changes of a smoothed spline error curve at sub-intervals during training . We compare the proposed approach against hard parameter sharing and soft parameter sharing transfer methods in the few-shot learning case . We also compare against models that are fully trained on the target task in the standard supervised learning setup . Model-agnostic meta-learning ( MAML ) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks . The MAML algorithm performs well on few-shot learning problems in classification , regression , and fine-tuning of policy gradients in reinforcement learning , but comes with the need for costly hyperparameter tuning for training stability . By combining { \em Shoestring } with graph convolutional networks , label propagation and their recent label-efficient variations ( IGCN and GLP ) , we are able to achieve state-of-the-art node classification performance in the presence of very few labeled samples . In addition , we demonstrate the effectiveness of our framework on image classification tasks in the few-shot learning regime , with significant gains on miniImageNet ( $ 2.57\%\sim3.59\%$ ) and tieredImageNet ( $ 1.05\%\sim2.70\%$ ) . While considerable progress has been achieved in few-shot learning from a few image examples , much less attention has been given to the verbal descriptions that are usually provided to infants when they are presented with a new object . In this paper , we focus on the role of additional semantics that can significantly facilitate few-shot visual learning . Building upon recent advances in few-shot learning with additional semantic information , we demonstrate that further improvements are possible using richer semantics and multiple semantic sources . document : Learning to Compare : Relation Network for Few - Shot Learning We present a conceptually simple , flexible , and general framework for few - shot learning , where a classifier must learn to recognise new classes given only few examples from each . Our method , called the Relation Network ( RN ) , is trained end - to - end from scratch . section : Abstract Meta - learning for few - shot learning entails acquiring a prior over previous tasks and experiences , such that new tasks be learned from small amounts of data . However , a critical challenge in few - shot learning is task ambiguity : even when a powerful prior can be meta - learned from a large number of prior tasks , a small dataset for a new task can simply be too ambiguous to acquire a single model ( e.g. , a classifier ) for that task that is accurate . Finally , while we do not experiment with this in this paper , we expect that modeling this ambiguity will be helpful for reinforcement learning problems , where it can be used to aid in exploration . While recognizing and accounting for ambiguity is an important aspect of the few - shot learning problem , it is particularly challenging to model when scaling to high - dimensional data , large function approximators , and multimodal task structure . Representing distributions over functions is relatively straightforward when using simple function approximators , such as linear functions , and has been done extensively in early few - shot learning approaches using Bayesian models Our experiments show that our approach improves on MAML [ reference ] , which outperforms the model by Edwards and Storkey [ reference ] . Other work that considers model uncertainty in the few - shot learning setting is the LLAMA method [ reference ] , which also builds on the MAML algorithm . LLAMA makes use of a local Laplace approximation for modeling the task parameters ( post - update parameters ) , which introduces the need to approximate a high dimensional covariance matrix . Going forward our approach will naturally benefit from advances in generative models as we can simply upgrade our base generative model , and so future work will pursue this . Compared with some other approaches in the literature for few - shot learning , our requirement for supervision is weaker : we only ask at training time that we are given datasets , but we do not need labels for the datasets , nor even information on whether two datasets represent the same or different classes . It would be interesting then to explore application areas where only this weaker form of supervision is available . Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning Require : p ( T ) : distribution over tasks Require : α , β : step size hyperparameters 1 : randomly initialize θ 2 : while not done do 3 : One-shot learning is usually tackled by using generative models or discriminative embeddings . Discriminative methods based on deep learning , which are very effective in other learning scenarios , are ill-suited for one-shot learning as they need large amounts of training data . Second , we introduce the recurrence analysis for qualitative and quantitative description of the gesture input and the system dynamics of an ESN , and show that the methodology complements classic stability measures . Finally , we address the reservoir itself and propose an algorithm for pruning connectivity in a one-shot learning scenario . The neighbourhood information is gradually aggregated to the root node in a bottom-up manner , and aggregation weights are supervised by the class relationships between the nodes . We apply MNE on image search and few shot learning tasks . Extensive ablation studies demonstrate the effectiveness of each component , and our method significantly outperforms the state-of-the-art approaches . In this paper , a novel change detection framework based on meta-learning , called MLCD , is proposed . We model the change detection problem as a one-shot learning problem , although there are no labels for reference in reality . Inspired by active learning , we present two sample selection strategies to find a few representative samples . Furthermore , this paper presents an end-to-end learning framework for training adaptive kernel SVMs , which eliminates the problem of choosing a correct kernel and good features for SVMs . Next , the one-shot learning problem is redefined for audio signals . Then the model was tested on vision task ( using Omniglot dataset ) and speech task ( using TIMIT dataset ) as well . The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available . A prototypical example of this is the one-shot learning setting , in which we must correctly make predictions given only a single example of each new class . In this paper , we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs . We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process . Such encoded HD vector is processed as a single unit for fast one-shot learning , and robust classification . It can be interpreted to identify the most useful features as well . In the scenario of one/multi-shot learning , conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals . Thus , given a CNN pre-trained for object classification , this paper proposes a method that first summarizes the knowledge hidden inside the CNN into a dictionary of latent activation patterns , and then builds a new model for part localization by manually assembling latent patterns related to the target part via human interactions . The main contribution of the paper is an effective real-time system for one-shot action modeling and recognition ; the paper highlights the effectiveness of sparse coding techniques to represent 3D actions . We obtain very good results on three different data sets : a benchmark data set for one-shot action learning ( the ChaLearn Gesture Data Set ) , an in-house data set acquired by a Kinect sensor including complex actions and gestures differing by small details , and a data set created for human-robot interaction purposes . Finally we demonstrate that our system is effective also in a human-robot interaction setting and propose a memory game , " All Gestures You Can " , to be played against a humanoid robot . Previous work to learn tasks through observing a demonstration ( e.g. , Lent & Laird , 2001 ) has required observing many examples of the same task . One-shot learning of tasks presents a significant challenge because the observed sequence is inherently incomplete -- the user only performs the steps required for the current situation . Furthermore , their decision-making processes , which reflect the control structures in the procedure , are not revealed . We model a " one-shot learning " situation , where very few observations y1, ... ,yn ∈ ℝd are available . Associated with each observation yi is a very high-dimensional vector xi ∈ ℝd , which provides context for yi and enables us to predict subsequent observations , given their own context . Thus to train our network to do rapid learning , we train it by showing only a few examples per class , switching the task from minibatch to minibatch , much like how it will be tested when presented with a few examples of a new task . Besides our contributions in defining a model and training criterion amenable for one - shot learning , we contribute by the definition of tasks that can be used to benchmark other approaches on both ImageNet and small scale language modeling . We hope that our results will encourage others to work on this challenging problem . We define the mapping to be where is parameterised by a neural network . Thus , when given a new support set of examples from which to one - shot learn , we simply use the parametric neural network defined by to make predictions about the appropriate label for each test example : . In general , our predicted output class for a given input unseen example and a support set becomes . Lake et al . [ reference ] have been able to learn representations using probabilistic inference over Bayesian programs , which achieved convincing one - shot learning results on the OMNI dataset . In addition , prior research attempted to learn disentangled representations using supervised data . In this work , we investigate the Active Meta-Learning combined with datasetoids , focusing on using the Random forest algorithm in meta-learning . Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples and obtain a significant gain in Meta-Learning performance . In a previous work , we proposed the Active Meta-Learning , in which Active Learning was used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation . In the current work , we extend our previous research by combining different Uncertainty Sampling methods for Active Meta-Learning , considering that each individual method will provide useful information to select relevant problems . We also investigated the use of Outlier Detection to remove a priori those problems considered as outliers , aiming to improve the performance of the sampling methods . The background and structure of meta-learning architectures is discussed , incorporating viewpoints from psychology , sociology , computational intelligence , and engineering . The benefits and limitations of meme-based learning are demonstrated through two experimental case studies – Meta-Learning Genetic Programming and MetaLearning Traveling Salesman Problem Optimization . Additionally , the development and properties of several new algorithms are detailed , inspired by the previous case-studies . In this work , we proposed the Active Meta-Learning , in which Active Learning techniques are used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation . In an implemented prototype , we evaluated the use of two different Active Learning techniques applied in two different Meta-Learning tasks . The performed experiments revealed a significant gain in the Meta-Learning performance when the active techniques were used to support the meta-example generation . Training on all such sequences of domains provides dramatically more ` practice ' for a base DG learner compared to existing approaches , thus improving performance on a true testing domain . This strategy can be instantiated for different base DG algorithms , but we focus on its application to the recently proposed Meta-Learning Domain generalization ( MLDG ) . We show that for MLDG it leads to a simple to implement and fast algorithm that provides consistent performance improvement on a variety of DG benchmarks . This paper introduces a new framework for data efficient and versatile learning . Specifically : 1 ) We develop ML-PIP , a general framework for Meta-Learning approximate Probabilistic Inference for Prediction . ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods . Few-shot relation classification describes a circumstance where a model is required to classify new-coming query instances after meeting only few support instances during testing . In this paper , we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of training data . We also show that a range of different reasoning skills are needed to solve our task . These results indicate that few-shot relation classification remains an open problem and still requires further research . Our detailed analysis points multiple directions for future research . In effect , our method trains the model to be easy to fine-tune . We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks , produces good results on few-shot regression , and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies . This approach is straightforward to implement , provides uncertainty quantification , and does not require estimation of task-specific parameters . We empirically demonstrate that the proposed method outperforms several state-of-the-art algorithms in few-shot regression , classification , and cross-domain adaptation . Moreover , we theoretically justify the benefit of the learnt prior hypothesis for fast adaptation to new few-shot learning tasks via minibatch proximal update . Experimental results on several few-shot regression and classification tasks demonstrate the advantages of our method over state-of-the-arts . The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks . Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better , or at least competitive , performance compared to existing meta-learning alternatives . It is challenging to learn accurate representations of these words with only a few observations . In this paper , we formulate the learning of OOV embeddings as a few-shot regression problem , and address it by training a representation function to predict the oracle embedding vector ( defined as embedding trained with abundant observations ) based on limited observations . Specifically , we propose a novel hierarchical attention-based architecture to serve as the neural regression function , with which the context information of a word is encoded and aggregated from K observations . In this work , we propose a new approach that affords fast adaptation to previously unseen cameras , and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets . We present a general approach that utilizes the concept of color temperature to frame color constancy as a set of distinct , homogeneous few-shot regression tasks , each associated with an intuitive physical meaning . We integrate this novel formulation within a meta-learning framework , enabling fast generalisation to previously unseen cameras using only handfuls of camera specific training samples . Compared to single deep kernel learning , our novel algorithm permits finding the appropriate kernel for each task during inference , rather than using the same for all tasks . As such , our algorithm performs more effectively with complex task distributions in few-shot learning , which we demonstrate by benchmarking against existing state-of-the-art algorithms using real-world , few-shot regression tasks related to drug discovery . In effect , our method trains the model to be easy to fine - tune . We demonstrate that this approach leads to state - of - the - art performance on two fewshot image classification benchmarks , produces good results on few - shot regression , and accelerates fine - tuning for policy gradient reinforcement learning with neural network policies . section : Introduction This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy ( MCE ) IRL framework . Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve . We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms . While there has been exciting progress in this direction , most of the approaches still require a few hundred tasks for meta-training , which limits the scalability of the approaches . Our main contribution is to formulate one-shot imitation learning as a symbolic planning problem along with the symbol grounding problem . This formulation disentangles the policy execution from the inter-task generalization and leads to better data efficiency . The robotic hardware of our custom built robot Lisa and the PAL Robotics TIAGo , both running the same methods , are presented . New approaches for object recognition , especially the preprocessed segment augmentation , effort based gripping , gesture recognition and approaches for visual imitation learning based on continuous spatial observations between a demonstrator and the interacting objects are presented . Further , we present the current state of research of our Imitation Learning approaches , where we propose a hybrid benchmark and methods for bootstrapping actions . For anomalous action research , it is difficult to collect and make datasets due to the rare occurrence of anomalous actions . Therefore , the research on few-shot learning of action recognition has certain significance . Based on the above , this paper studies the few-shot learning of anomalous action recognition . Notably , we achieve the accuracy of 87.75 % on 5-shot miniImageNet which approximately outperforms existing methods by 10 % . We also conduct experiment on cross-domain few-shot tasks and provide the best results .
Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences , such that new tasks be learned from small amounts of data . However , a critical challenge in few-shot learning is task ambiguity : even when a powerful prior can be meta-learned from a large number of prior tasks , a small dataset for a new task can simply be too ambiguous to acquire a single model ( e.g. , a classifier ) for that task that is accurate . In this paper , we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution .
Few-shot learning in computer vision is a very challenging task . Many approaches have been proposed to tackle the few-shot learning problem . Meta learning , a method of learning to learn , is introduced into few-shot learning problem and has achieved pretty good results .
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting . The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available .
The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled . In this few-shot learning scenario , alignment and separation of semantic probability distributions is difficult because of the lack of data . We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes , it is possible to effectively address the supervised adaptation problem .
This is achieved by using a decaying parameter chosen according to the slope changes of a smoothed spline error curve at sub-intervals during training . We compare the proposed approach against hard parameter sharing and soft parameter sharing transfer methods in the few-shot learning case . We also compare against models that are fully trained on the target task in the standard supervised learning setup .
Model-agnostic meta-learning ( MAML ) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks . The MAML algorithm performs well on few-shot learning problems in classification , regression , and fine-tuning of policy gradients in reinforcement learning , but comes with the need for costly hyperparameter tuning for training stability .
By combining { \em Shoestring } with graph convolutional networks , label propagation and their recent label-efficient variations ( IGCN and GLP ) , we are able to achieve state-of-the-art node classification performance in the presence of very few labeled samples . In addition , we demonstrate the effectiveness of our framework on image classification tasks in the few-shot learning regime , with significant gains on miniImageNet ( $ 2.57\%\sim3.59\%$ ) and tieredImageNet ( $ 1.05\%\sim2.70\%$ ) .
While considerable progress has been achieved in few-shot learning from a few image examples , much less attention has been given to the verbal descriptions that are usually provided to infants when they are presented with a new object . In this paper , we focus on the role of additional semantics that can significantly facilitate few-shot visual learning . Building upon recent advances in few-shot learning with additional semantic information , we demonstrate that further improvements are possible using richer semantics and multiple semantic sources .
document : Learning to Compare : Relation Network for Few - Shot Learning We present a conceptually simple , flexible , and general framework for few - shot learning , where a classifier must learn to recognise new classes given only few examples from each . Our method , called the Relation Network ( RN ) , is trained end - to - end from scratch .
section : Abstract Meta - learning for few - shot learning entails acquiring a prior over previous tasks and experiences , such that new tasks be learned from small amounts of data . However , a critical challenge in few - shot learning is task ambiguity : even when a powerful prior can be meta - learned from a large number of prior tasks , a small dataset for a new task can simply be too ambiguous to acquire a single model ( e.g. , a classifier ) for that task that is accurate .
Finally , while we do not experiment with this in this paper , we expect that modeling this ambiguity will be helpful for reinforcement learning problems , where it can be used to aid in exploration . While recognizing and accounting for ambiguity is an important aspect of the few - shot learning problem , it is particularly challenging to model when scaling to high - dimensional data , large function approximators , and multimodal task structure . Representing distributions over functions is relatively straightforward when using simple function approximators , such as linear functions , and has been done extensively in early few - shot learning approaches using Bayesian models
Our experiments show that our approach improves on MAML [ reference ] , which outperforms the model by Edwards and Storkey [ reference ] . Other work that considers model uncertainty in the few - shot learning setting is the LLAMA method [ reference ] , which also builds on the MAML algorithm . LLAMA makes use of a local Laplace approximation for modeling the task parameters ( post - update parameters ) , which introduces the need to approximate a high dimensional covariance matrix .
Going forward our approach will naturally benefit from advances in generative models as we can simply upgrade our base generative model , and so future work will pursue this . Compared with some other approaches in the literature for few - shot learning , our requirement for supervision is weaker : we only ask at training time that we are given datasets , but we do not need labels for the datasets , nor even information on whether two datasets represent the same or different classes . It would be interesting then to explore application areas where only this weaker form of supervision is available .
Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning Require : p ( T ) : distribution over tasks Require : α , β : step size hyperparameters 1 : randomly initialize θ 2 : while not done do 3 :
One-shot learning is usually tackled by using generative models or discriminative embeddings . Discriminative methods based on deep learning , which are very effective in other learning scenarios , are ill-suited for one-shot learning as they need large amounts of training data .
Second , we introduce the recurrence analysis for qualitative and quantitative description of the gesture input and the system dynamics of an ESN , and show that the methodology complements classic stability measures . Finally , we address the reservoir itself and propose an algorithm for pruning connectivity in a one-shot learning scenario .
The neighbourhood information is gradually aggregated to the root node in a bottom-up manner , and aggregation weights are supervised by the class relationships between the nodes . We apply MNE on image search and few shot learning tasks . Extensive ablation studies demonstrate the effectiveness of each component , and our method significantly outperforms the state-of-the-art approaches .
In this paper , a novel change detection framework based on meta-learning , called MLCD , is proposed . We model the change detection problem as a one-shot learning problem , although there are no labels for reference in reality . Inspired by active learning , we present two sample selection strategies to find a few representative samples .
Furthermore , this paper presents an end-to-end learning framework for training adaptive kernel SVMs , which eliminates the problem of choosing a correct kernel and good features for SVMs . Next , the one-shot learning problem is redefined for audio signals . Then the model was tested on vision task ( using Omniglot dataset ) and speech task ( using TIMIT dataset ) as well .
The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available . A prototypical example of this is the one-shot learning setting , in which we must correctly make predictions given only a single example of each new class . In this paper , we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs .
We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process . Such encoded HD vector is processed as a single unit for fast one-shot learning , and robust classification . It can be interpreted to identify the most useful features as well .
In the scenario of one/multi-shot learning , conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals . Thus , given a CNN pre-trained for object classification , this paper proposes a method that first summarizes the knowledge hidden inside the CNN into a dictionary of latent activation patterns , and then builds a new model for part localization by manually assembling latent patterns related to the target part via human interactions .
The main contribution of the paper is an effective real-time system for one-shot action modeling and recognition ; the paper highlights the effectiveness of sparse coding techniques to represent 3D actions . We obtain very good results on three different data sets : a benchmark data set for one-shot action learning ( the ChaLearn Gesture Data Set ) , an in-house data set acquired by a Kinect sensor including complex actions and gestures differing by small details , and a data set created for human-robot interaction purposes . Finally we demonstrate that our system is effective also in a human-robot interaction setting and propose a memory game , " All Gestures You Can " , to be played against a humanoid robot .
Previous work to learn tasks through observing a demonstration ( e.g. , Lent & Laird , 2001 ) has required observing many examples of the same task . One-shot learning of tasks presents a significant challenge because the observed sequence is inherently incomplete -- the user only performs the steps required for the current situation . Furthermore , their decision-making processes , which reflect the control structures in the procedure , are not revealed .
We model a " one-shot learning " situation , where very few observations y1, ... ,yn ∈ ℝd are available . Associated with each observation yi is a very high-dimensional vector xi ∈ ℝd , which provides context for yi and enables us to predict subsequent observations , given their own context .
Thus to train our network to do rapid learning , we train it by showing only a few examples per class , switching the task from minibatch to minibatch , much like how it will be tested when presented with a few examples of a new task . Besides our contributions in defining a model and training criterion amenable for one - shot learning , we contribute by the definition of tasks that can be used to benchmark other approaches on both ImageNet and small scale language modeling . We hope that our results will encourage others to work on this challenging problem .
We define the mapping to be where is parameterised by a neural network . Thus , when given a new support set of examples from which to one - shot learn , we simply use the parametric neural network defined by to make predictions about the appropriate label for each test example : . In general , our predicted output class for a given input unseen example and a support set becomes .
Lake et al . [ reference ] have been able to learn representations using probabilistic inference over Bayesian programs , which achieved convincing one - shot learning results on the OMNI dataset . In addition , prior research attempted to learn disentangled representations using supervised data .
In this work , we investigate the Active Meta-Learning combined with datasetoids , focusing on using the Random forest algorithm in meta-learning . Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples and obtain a significant gain in Meta-Learning performance .
In a previous work , we proposed the Active Meta-Learning , in which Active Learning was used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation . In the current work , we extend our previous research by combining different Uncertainty Sampling methods for Active Meta-Learning , considering that each individual method will provide useful information to select relevant problems . We also investigated the use of Outlier Detection to remove a priori those problems considered as outliers , aiming to improve the performance of the sampling methods .
The background and structure of meta-learning architectures is discussed , incorporating viewpoints from psychology , sociology , computational intelligence , and engineering . The benefits and limitations of meme-based learning are demonstrated through two experimental case studies – Meta-Learning Genetic Programming and MetaLearning Traveling Salesman Problem Optimization . Additionally , the development and properties of several new algorithms are detailed , inspired by the previous case-studies .
In this work , we proposed the Active Meta-Learning , in which Active Learning techniques are used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation . In an implemented prototype , we evaluated the use of two different Active Learning techniques applied in two different Meta-Learning tasks . The performed experiments revealed a significant gain in the Meta-Learning performance when the active techniques were used to support the meta-example generation .
Training on all such sequences of domains provides dramatically more ` practice ' for a base DG learner compared to existing approaches , thus improving performance on a true testing domain . This strategy can be instantiated for different base DG algorithms , but we focus on its application to the recently proposed Meta-Learning Domain generalization ( MLDG ) . We show that for MLDG it leads to a simple to implement and fast algorithm that provides consistent performance improvement on a variety of DG benchmarks .
This paper introduces a new framework for data efficient and versatile learning . Specifically : 1 ) We develop ML-PIP , a general framework for Meta-Learning approximate Probabilistic Inference for Prediction . ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods .
Few-shot relation classification describes a circumstance where a model is required to classify new-coming query instances after meeting only few support instances during testing . In this paper , we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of training data .
We also show that a range of different reasoning skills are needed to solve our task . These results indicate that few-shot relation classification remains an open problem and still requires further research . Our detailed analysis points multiple directions for future research .
In effect , our method trains the model to be easy to fine-tune . We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks , produces good results on few-shot regression , and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies .
This approach is straightforward to implement , provides uncertainty quantification , and does not require estimation of task-specific parameters . We empirically demonstrate that the proposed method outperforms several state-of-the-art algorithms in few-shot regression , classification , and cross-domain adaptation .
Moreover , we theoretically justify the benefit of the learnt prior hypothesis for fast adaptation to new few-shot learning tasks via minibatch proximal update . Experimental results on several few-shot regression and classification tasks demonstrate the advantages of our method over state-of-the-arts .
The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks . Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better , or at least competitive , performance compared to existing meta-learning alternatives .
It is challenging to learn accurate representations of these words with only a few observations . In this paper , we formulate the learning of OOV embeddings as a few-shot regression problem , and address it by training a representation function to predict the oracle embedding vector ( defined as embedding trained with abundant observations ) based on limited observations . Specifically , we propose a novel hierarchical attention-based architecture to serve as the neural regression function , with which the context information of a word is encoded and aggregated from K observations .
In this work , we propose a new approach that affords fast adaptation to previously unseen cameras , and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets . We present a general approach that utilizes the concept of color temperature to frame color constancy as a set of distinct , homogeneous few-shot regression tasks , each associated with an intuitive physical meaning . We integrate this novel formulation within a meta-learning framework , enabling fast generalisation to previously unseen cameras using only handfuls of camera specific training samples .
Compared to single deep kernel learning , our novel algorithm permits finding the appropriate kernel for each task during inference , rather than using the same for all tasks . As such , our algorithm performs more effectively with complex task distributions in few-shot learning , which we demonstrate by benchmarking against existing state-of-the-art algorithms using real-world , few-shot regression tasks related to drug discovery .
In effect , our method trains the model to be easy to fine - tune . We demonstrate that this approach leads to state - of - the - art performance on two fewshot image classification benchmarks , produces good results on few - shot regression , and accelerates fine - tuning for policy gradient reinforcement learning with neural network policies . section : Introduction
This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy ( MCE ) IRL framework . Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve . We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms .
While there has been exciting progress in this direction , most of the approaches still require a few hundred tasks for meta-training , which limits the scalability of the approaches . Our main contribution is to formulate one-shot imitation learning as a symbolic planning problem along with the symbol grounding problem . This formulation disentangles the policy execution from the inter-task generalization and leads to better data efficiency .
The robotic hardware of our custom built robot Lisa and the PAL Robotics TIAGo , both running the same methods , are presented . New approaches for object recognition , especially the preprocessed segment augmentation , effort based gripping , gesture recognition and approaches for visual imitation learning based on continuous spatial observations between a demonstrator and the interacting objects are presented . Further , we present the current state of research of our Imitation Learning approaches , where we propose a hybrid benchmark and methods for bootstrapping actions .
For anomalous action research , it is difficult to collect and make datasets due to the rare occurrence of anomalous actions . Therefore , the research on few-shot learning of action recognition has certain significance . Based on the above , this paper studies the few-shot learning of anomalous action recognition .
Notably , we achieve the accuracy of 87.75 % on 5-shot miniImageNet which approximately outperforms existing methods by 10 % . We also conduct experiment on cross-domain few-shot tasks and provide the best results .
Probabilistic Model-Agnostic Meta-Learning Only Image Cosine Embedding for Few-Shot Learning Meta-Transfer Learning through Hard Tasks Few-Shot Adversarial Domain Adaptation Dropping Networks for Transfer Learning Alpha MAML: Adaptive Model-Agnostic Meta-Learning Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data Baby steps towards few-shot learning with multiple semantics Learning to Compare: Relation Network for Few-Shot Learning Probabilistic Model-Agnostic Meta-Learning Towards a Neural Statistician Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Learning feed-forward one-shot learners Inspection of echo state networks for dynamic gestures Memory-Based Neighbourhood Embedding for Visual Recognition A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images Make SVM great again with Siamese kernel for few-shot learning Siamese Neural Networks for One-Shot Image Recognition Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals Interactively Transferring CNN Patterns for Part Localization Keep it simple and sparse: real-time action recognition Demonstration of PLOW: A Dialogue System for One-Shot Task Learning One-shot learning and big data with n=2 Matching Networks for One Shot Learning InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Active selection of training instances for a random forest meta-learner Combining Uncertainty Sampling methods for supporting the generation of meta-examples Meta-learning computational intelligence architectures Selective generation of training examples in active meta-learning Sequential Learning for Domain Generalization Meta-Learning Probabilistic Inference for Prediction MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data FewRel: A Large-Scale Supervised Few-shot Relation Classification Dataset with State-of-the-Art Evaluation Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Deep Kernel Transfer in Gaussian Processes for Few-shot Learning Efficient Meta Learning via Minibatch Proximal Update Learning to Learn Kernels with Variational Random Features Few-Shot Representation Learning for Out-Of-Vocabulary Words Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem. Adaptive Deep Kernel Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Multi-task Maximum Entropy Inverse Reinforcement Learning Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning homer@UniKoblenz: Winning Team of the RoboCup@Home Open Platform League 2018 Anomalous Action Recognition Research for Few-shot Learning Fast and Generalized Adaptation for Few-Shot Learning
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ICONIP (2) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NeurIPS ICLR ICML ICDM Workshops HLT-NAACL (Demonstrations) NIPS NIPS NIPS IJCNN ICLR (Poster) ICML NeurIPS ACL (1) ICML RoboCup
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