SYSTEMS AND METHODS FOR LEARNING RICH NEAREST NEIGHBOR REPRESENTATIONS FROM SELF-SUPERVISED ENSEMBLES

1 . A method for training a model for computing an ensemble vector representation of a plurality of pre-trained feature extractors comprising:

receiving, via a communication interface, a dataset of a plurality of data samples;

determining, in response to a sample from the dataset, a set of feature vectors via a plurality of pre-trained feature extractors, respectively;

retrieving a memory bank vector that is initialized corresponding to the plurality of data samples from the dataset;

mapping, via a plurality of Multi-Layer-Perceptron (MLPs), the memory bank vector into a plurality of mapped representations, respectively;

computing a loss objective between the set of feature vectors and the plurality of mapped representations; and

updating the plurality of MLPs and the memory bank vector based on the computed loss objective.

2 . The method of claim 1 , wherein the plurality of pre-trained feature extractors is selected from one or more of the pre-trained feature extractors that include different head architectures.

3 . The method of claim 1 , wherein the plurality of pre-trained feature extractors is selected from one or more of the pre-trained feature extractors that are trained on different objectives.

4 . The method of claim 1 , wherein the dataset includes a plurality of images.

5 . The method of claim 1 , wherein the dataset includes a plurality of text documents or a plurality of audio files.

6 . The method of claim 1 , wherein the dataset includes a plurality of point clouds or polygon meshes.

7 . The method of claim 1 , wherein the method further comprises:

freezing the parameters of the plurality of updated MLPs.

8 . A method for computing via a trained model an ensemble vector representation of a plurality of pre-trained feature vectors comprising:

receiving, via a communication interface, an interpretation data sample;

determining, in response to the interpretation data sample, a set of feature vectors via a plurality of pre-trained feature extractors, respectively;

determining an average of the set of feature vectors;

mapping, via a plurality of Multi-Layer-Perceptron (MLPs), the initialized memory bank vector into a plurality of mapped representations, respectively;

computing a loss objective between the set of feature vectors and the plurality of mapped representations; and

updating the initialized memory bank vector based on the computed loss objective while freezing the plurality of MLPs.

9 . The method of claim 8 , wherein the plurality of pre-trained feature extractors is selected from one or more of the pre-trained feature extractors that are trained on different objectives.

10 . The method of claim 8 , wherein the data sample includes a plurality of images.

11 . The method of claim 8 , wherein the data sample includes a plurality of text documents or a plurality of audio files.

12 . The method of claim 8 , wherein the data sample includes a plurality of point clouds or polygon meshes.

13 . A system for training a model for computing an ensemble of unsupervised vector representations, the system comprising:

a communication interface for receiving a query for information;

a memory storing a plurality of machine-readable instructions; and

a processor reading and executing the instructions from the memory to perform operations comprising:

receive, via a communication interface, a dataset of a plurality of data samples;

determine, in response to an input data sample from the dataset, a set of feature vectors via a plurality of pre-trained feature extractors, respectively;

retrieve a set of memory bank vectors that correspond to the input data sample;

generate, via a plurality of Multi-Layer-Perceptrons (MLPs), a mapped set of representations in response to an input of the set of memory bank vectors, respectively;

compute a loss objective between the set of feature vectors and the combination of the mapped set of representations and a network of layers in the MLP; and

update the plurality of MLPs and the memory bank vectors by minimizing the computed loss objective.

14 . The system of claim 11 , wherein the plurality of pre-trained feature extractors is selected from one or more of the pre-trained feature extractors that include different head architectures.

15 . The system of claim 11 , wherein the plurality of pre-trained feature extractors is selected from one or more of the pre-trained feature extractors that are trained on different objectives.

16 . The system of claim 12 , wherein the dataset includes a plurality of images.

17 . The system of claim 12 , wherein the dataset includes a plurality of text documents or a plurality of audio files.

18 . The system of claim 12 , wherein the dataset includes a plurality of point clouds or polygon meshes.

19 . The system of claim 12 , wherein the plurality of pre-trained feature extractors is selected from a plurality of convolutional neural network.

20 . The system of claim 11 , including further instructions to perform operations comprising:

freezing, the parameters of the plurality of updated MLPs;

receiving, via a communication interface, an interpretation data sample;

determining, in response to the interpretation data sample, a set of feature vectors via a plurality of pre-trained feature extractors, respectively;

updating the memory bank vector using an average of the set of feature vectors;

mapping, via a plurality of Multi-Layer-Perceptron (MLPs), the initialized memory bank vector into a plurality of mapped representations, respectively;

computing a loss objective between the set of feature vectors and the plurality of mapped representations; and

updating the initialized memory bank vector based on the computed loss objective while freezing the plurality of MLPs.